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Nested-loop neural network vector control of permanent magnet synchronous motors
Abstract
With the improvement of battery technology over the past two decades and automotive technology advances, more and more vehicle manufacturers have joined in the race to produce new generation of affordable, high-performance Electric Drive Vehicles (EDVs). Permanent Magnet Synchronous Motors (PMSMs) are at the top of AC motors in high performance drive systems for EDVs. Traditionally, a PMSM is controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show serious limitations. This paper investigates how to mitigate such problems using a nested-loop neural network architecture to control a PMSM. The neural network implements a dynamic programming algorithm and is trained using backpropagation through time. The performance of the neural controller is studied for typical vector control conditions and compared with conventional vector control methods, which demonstrates the neural vector control strategy proposed in this paper is effective. Even in a highly dynamic switching environment, the neural vector controller shows strong ability to track rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for complex EDV drive needs.
... However, only a few studies have investigated the challenges encountered in power electronic pulse width modulation (PWM) with the current control through the ADP-based controller [13]. Some studies have [13,14] proposed and validated the vector control of a grid-connected rectifier/inverter using ANN and backpropagation through the time weight updating rule. ...
... .(14). Single line diagram of DSTATCOM with nonlinear loads. ...
Background & Objective
Harmonic amplification is one of the primary issues in power system networks. The objective of this study is to manage the harmonic event and its significant effects on power quality. A new control approach that uses Artificial Intelligence (AI) is proposed and applied to a Distribution Static Synchronous Compensator (DSTATCOM). DSTATCOM is a FACTS device that can achieve highly effective reactive power compensation to reduce and/or damp the harmonic amplification in power system networks.
Results & Conclusion
Simulation results are obtained using the MATLAB/Simulink package. The validity and effectiveness of using the AI approach are proven based on the DSTATCOM FACTs device with linear and nonlinear loads. Analysis results are discussed.
... Combining parametric structure with incremental optimization forms a new class of ADP called adaptive critic design (ACD) that approximate the optimal cost [12]- [13]. Heuristic dynamic programing (HDP) is the simplest form of ACDs and has been studied in different applications, such as vector control of a grid-connected converter [14], power system stability [15], and permanent magnet synchronous motor drive [16]. ...
In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage and/or reactive power controllers in virtual inertia control schemes.
... Combining parametric structure with incremental optimization forms a new class of ADP called adaptive critic design (ACD) that approximate the optimal cost [7]- [8]. Heuristic dynamic programing (HDP) is the simplest form of ACDs and has been studied in different applications, such as vector control of a grid-connected converter [9], power system stability [10], and permanent magnet synchronous motor drive [11]. ...
In this study, a heuristic dynamic programming controller is proposed to control a boost converter. Conventional controllers such as proportional integral-derivative (PID) or proportional integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during the start-up, the load change, or the input voltage variation is not optimal since the system model changes by varying the operating point. The heuristic dynamic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the HDP is that the neural network based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. An HDP with a well trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the HDP boost converter, the simulation results are provided.
... Combining parametric structure with incremental optimization forms a new class of ADP called adaptive critic design (ACD) that approximate the optimal cost [7]- [8]. Heuristic dynamic programing (HDP) is the simplest form of ACDs and has been studied in different applications, such as vector control of a grid-connected converter [9], power system stability [10], and permanent magnet synchronous motor drive [11]. ...
In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia based control of grid connected three phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non inductive grids. A neural network based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks, critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct fed voltage and reactive power controllers in virtual inertia control schemes.
... Yet, only a few papers have investigated the power electronics pulse width modulation (PWM) current control through the ADP based controller concerning the difficulty involved as mentioned in [39]. References [39]− [42] proposed and validated the vector control of a grid-connected rectifier/inverter using an artificial neural network and back-propagation through time weights updating rule. However, the system was not implemented for harmonic reduction applications and the current controller lacks the online learning capability. ...
Nonlinear loads in the power distribution system
cause non-sinusoidal currents and voltages with harmonic components.
Shunt active filters (SAF) with current controlled voltage
source inverters (CCVSI) are usually used to obtain balanced and
sinusoidal source currents by injecting compensation currents.
However, CCVSI with traditional controllers have a limited
transient and steady state performance. In this paper, we propose
an adaptive dynamic programming (ADP) controller with online
learning capability to improve transient response and harmonics.
The proposed controller works alongside existing proportional
integral (PI) controllers to efficiently track the reference currents
in the d − q domain. It can generate adaptive control actions to
compensate the PI controller. The proposed system was simulated
under different nonlinear (three-phase full wave rectifier) load
conditions. The performance of the proposed approach was
compared with the traditional approach. We have also included
the simulation results without connecting the traditional PI
control based power inverter for reference comparison. The
online learning based ADP controller not only reduced average
total harmonic distortion by 18.41 %, but also outperformed
traditional PI controllers during transients.
The auxiliary activity family 9 (AA9) copper-dependent lytic polysaccharide monooxygenase (LPMO) from Trichoderma reesei (EG4; TrCel61A) was investigated for its ability to oxidize the complex polysaccharides from soybean. The substrate specificity of the enzyme was assessed against a variety of substrates, including both soy spent flake, a by-product of the soy food industry, and soy spent flake pretreated with sodium hydroxide. Products from enzymatic treatments were analyzed using mass spectrometry and high performance anion exchange chromatography. We demonstrate that TrCel61A is capable of oxidizing cellulose from both pretreated soy spent flake and phosphoric acid swollen cellulose, oxidizing at both the C1 and C4 positions. In addition, we show that the oxidative activity of TrCel61A displays a synergistic effect capable of boosting endoglucanase activity, and thereby substrate depolymerization of soy cellulose, by 27%.
This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.
The interior permanent magnet synchronous motor (PMSM) can offer many advantages, including high power-to-weight ratio, high efficiency, rugged construction, low cogging torque and the capability of reluctance torque, so it is widely used in electric vehicle (EV). Two control schemes, namely field oriented control (FOC) and direct torque control (DTC) are used in PMSM drive. In order to decrease current and torque ripple and fix switching frequency, an improved DTC scheme based on the control of stator flux, torque angle and torque was proposed, which used voltage vector selection strategy and the technology of space vector modulation (SVM) to generate the applied voltage vector instead of switching table. And this paper compared these three control schemes based on a 15-kW interior PMSM used in Honda Civic 06My hybrid electrical vehicle. Experimental results show for the FOC using the hysteresis current control, due to the lower sampling period, stator current is more sinusoidal. But it needs the continuous rotor position information and the switching frequency of the VSI is not constant. For the DTC using switching table, current ripple is higher and the switching frequency of the VSI is also not constant. But it does not need the rotor position information except for the initial rotor position. Compared with switching table, the proposed DTC can decrease current and torque ripple and fix switching frequency.
An Interior Permanent Magnet Synchronous Motor (IPMSM) drive fed by Space Vector Modulated (SVM)
inverter without rotor position sensor is proposed in this paper. The control system estimates the motor stator flux and its position through a Real Time Recurrent Neural Network (RTRNN). The RTRNN is trained off-line to estimate stator flux from the speed error and stationary α- and β- axis current components obtained through Park’s transformation. Vector control of the motor is achieved through command torque and magnetizing current components generation and necessary transformations. The estimated flux and rotor angle are found
to match accurately with their corresponding actual values. A Proportional plus Integral (PI) controller is used to control the motor variables in closed loop. The effectiveness of the drive system is tested for different operating conditions. The proposed system is found to work satisfactorily under these conditions and perturbations of parameters.
Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations. This paper investigates how to mitigate such problems using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming (DP) algorithm and is trained using backpropagation through time. The performance of the DP-based neural controller is studied for typical vector control conditions and compared with conventional vector control methods. The paper also investigates how varying grid and power converter system parameters may affect the performance and stability of the neural control system. Future research issues regarding the control of grid-connected converters using DP-based neural networks are analyzed.
DIGITAL CONTROL OF DYNAMIC SYSTEMS By Gene F. Franklin, J. David Powell, and Michael Workman 3rd ed, 1998, Addison-Wesley, ISBN: 0-201-82054-4, acquired by Prentice-Hall, but they went out of print in 2006. Replaced by Ellis-Kagle Press: ISBN: 0-9791226-1-9 or ISBN13: 978-0-9791226-1-3 Ellis-Kagle Press was formed by Franklin and Powell in order to keep the book in print after 2006. However their book went out of print in 2021 and has been replaced by a PDF version which is available for download at: EllisKaglePress.com.
The current Ellis-Kagle Press PDF version of the 3rd edition has all known errors corrected as of 2021 and EllisKaglePress.com has the link for downloading the associated Matlab files. If you have questions or suggestions, please send to: EllisKagle@gmail.com
This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of the neural networks, thus avoiding risks of instability.
Selected adaptive critic (AC) methods are known to be capable of designing (approximately) optimal control policies for nonlinear plants. The present research focuses on an AC method known as dual heuristic programming (DHP). In particular it is seen as useful to explore correspondences between the form of a utility function and the resulting controllers designed by the DHP method. Based on the task of designing a steering controller for a 2-axle, terrestrial, autonomous vehicle, the present paper relates a pair of critics to “divide the labor” of training the controller. Improvements in convergence of the training process is realized in this way. The controllers designed by the DHP method are reasonably robust, and demonstrate good performance on disturbances not even trained on encountering a patch of ice during a steering maneuver, and encountering a wind gust perpendicular to direction of travel
In this paper, adaptive critic based neural networks have been
used to design a controller for a benchmark problem in aircraft
autolanding. The adaptive critic control methodology comprises
successive adaptations of two neural networks, namely
“action” and “critic” network (which approximate
the Hamiltonian equations associated with optimal control theory) until
closed loop optimal control is achieved. The autolanding problem deals
with longitudinal dynamics of an aircraft which is to be landed in a
specified touchdown region (within acceptable ranges of speed, pitch
angle and sink rate) in the presence of wind disturbances and gusts
using elevator deflection as the control for glideslope and flare modes.
The performance of the neurocontroller is compared to that of a
conventional proportional-integral-differential (PID) controller. The
results show that the neurocontrollers have good potential for aircraft
applications
This paper traces the development of neural-network (NN)-based feedback controllers that are derived from the principle of adaptive/approximate dynamic programming (ADP) and discusses their closed-loop stability. Different versions of NN structures in the literature, which embed mathematical mappings related to solutions of the ADP-formulated problems called ldquoadaptive criticsrdquo or ldquoaction-criticrdquo networks, are discussed. Distinction between the two classes of ADP applications is pointed out. Furthermore, papers in ldquomodel-freerdquo development and model-based neurocontrollers are reviewed in terms of their contributions to stability issues. Recent literature suggests that work in ADP-based feedback controllers with assured stability is growing in diverse forms.
The current waveforms for optimal excitation of surface-mounted
permanent-magnet synchronous machines are set forth. Four different
modes are considered, involving varying degrees of minimization of RMS
current and torque ripple. The optimized waveforms are markedly
different than the traditional sinusoidal or rectangular excitation
schemes. Inclusion of cogging torque and arbitrary degree of torque
ripple minimization generalize this work over that of previous authors.
An experimental drive and a detailed computer simulation verify the
proposed control schemes
This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming, a member of the adaptive Critic designs family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system and a 45-bus network (a section of the Brazilian power system). Simulation results are provided to show that the proposed controller outperforms a conventional PI controller in large scale faults as well as small disturbances
Basic backpropagation, which is a simple method now being widely
used in areas like pattern recognition and fault diagnosis, is reviewed.
The basic equations for backpropagation through time, and applications
to areas like pattern recognition involving dynamic systems, systems
identification, and control are discussed. Further extensions of this
method, to deal with systems other than neural networks, systems
involving simultaneous equations, or true recurrent networks, and other
practical issues arising with the method are described. Pseudocode is
provided to clarify the algorithms. The chain rule for ordered
derivatives-the theorem which underlies backpropagation-is briefly
discussed. The focus is on designing a simpler version of
backpropagation which can be translated into computer code and applied
directly by neutral network users
Half-Title Page Title Page Copyright Page Contents Preface Notation
Electric Vehicle (EV) is a dream for the human being city traffic without exhausting gas and with low noise. Permanent magnet synchronous motor (PMSW) became at the top of AC motors in high performance drive systems such as EV. This paper describes a full digital system to control a PMSM used in EV driving by TMS320F2812 DSP. Based on the analysis of the mathematical model of PMSM, the constant torque and flux-weakening control for electric vehicle are realized. The system will be tested under various operating conditions. Experimental results show that the system has high performance comparing with the design value, it also verified the correctness of the design of PMSM and the computing platform.
With the advance of power electronics, direct-driven permanent magnet synchronous generators have drawn increased interest to wind turbine manufacturers due to its advantages over other variable-speed wind turbines. This article studies permanent magnet synchronous generator characteristics under the general d-q control strategy in the rotor-flux-oriented frame so as to benefit the development of advanced permanent magnet synchronous generator control technology. Compared to traditional approaches, the specific features of the article are (1) a steady-state permanent magnet synchronous generator model in a d-q reference frame, (2) a simulation mechanism that reflects the general permanent magnet synchronous generator d-q control strategy, (3) an integrative study that combines generator-converted power with extracted wind power characteristics, and (4) a joint investigation incorporating both steady-state and transient evaluations. Extensive simulation-based analysis is conducted to study how permanent magnet synchronous generator characteristics are affected by different d-q control conditions and are interacted with wind power drive characteristics in power generation and speed regulation of a permanent magnet synchronous generator wind turbine.
A dual neural network architecture for the solution of aircraft control problems is presented. The neural network structure, consisting of an action network and a critic network, is used to approximately solve the dynamic programming equations associated with optimal control with a high degree of accuracy. Numerical results from applying this methodology to optimally control the longitudinal dynamics of an aircraft are presented. The novelty in this synthesis of the optimal controller network is that it needs no external training inputs; it needs no a priori knowledge of the form of control. Numerical experiments with neural-network-based control as well as other pointwise optimal control techniques are presented. These results show that this network architecture yields optimal control over the entire range of training. In other words, the neural network can function as an autopilot. A scalar problem is also used in this study for easier illustration of the solution development.
This well-respected work discusses the use of digital computers in the real-time control of dynamic systems. The emphasis is on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude.
MATLAB statements and problems are thoroughly and carefully integrated throughout the book to offer readers a complete design picture. Many of the figures in the book were created by Matlab files, which are available to download at the link below for interested readers.
In this article, we introduce some recent research trends within the field of adaptive/approximate dynamic programming (ADP), including the variations on the structure of ADP schemes, the development of ADP algorithms and applications of ADP schemes. For ADP algorithms, the point of focus is that iterative algorithms of ADP can be sorted into two classes: one class is the iterative algorithm with initial stable policy; the other is the one without the requirement of initial stable policy. It is generally believed that the latter one has less computation at the cost of missing the guarantee of system stability during iteration process. In addition, many recent papers have provided convergence analysis associated with the algorithms developed. Furthermore, we point out some topics for future studies.
Immunized computational systems combine a priori knowledge with
the adapting capabilities of immune systems to provide a powerful
alternative to currently available techniques for intelligent control.
In this paper, we present a perspective on various levels of intelligent
control and relate them to similar functioning in human immune systems.
A technique for implementing immunized computational systems as adaptive
critics is presented and the technique is then applied to a simple two
degree-of-freedom control problem as well as a more complex flight path
generator for level 2, nonlinear, full-envelope, intelligent aircraft
control
In recent years, sensorless control of interior permanent magnet synchronous motors (IPMSMs) has been a research area of significant interest. A multitude of solutions have been proposed for sensorless operation but most of them fail to deliver satisfactory performance in the low speed region. This study describes the active flux concept, which is used to design a rotor position and speed observer. The proposed observer does not require any speed adaptation. Hence, speed estimation errors especially in the low speed region do not affect the performance of the observer. An online stator resistance estimator is proposed to further enhance the low speed operation of the observer. The proposed observer is capable of delivering accurate state estimation at very low speeds including standstill without signal injection. Simulation and experimental results at 0 and 5 rpm confirm the effectiveness of the proposed sensorless approach.
With the advance of power electronic technology, direct driven permanent magnet synchronous generators (PMSG) have increasingly drawn interests to wind turbine manufactures. This paper studies and compares conventional and a novel control designs for a direct driven PMSG wind turbine. The paper presents transient and steady-state models of a PMSG system in a d–q reference frame. Then, general PMSG characteristics are investigated in the rotor-flux-oriented frame. A shortage of conventional control mechanisms is studied analytically and through computer simulation. A novel direct-current based d–q vector control technique is proposed by integrating fuzzy, adaptive and traditional PID control technologies in an optimal control configuration. Comparison study demonstrates that the proposed control approach, having superior performance in various aspects, is effective not only in achieving desired PMSG control objectives but also in improving the optimal performance of the overall system.
The three-phase grid-connected converter is widely used in renewable and electric power system applications. Traditionally, control of the three-phase grid-connected converter is based on the standard decoupled d–q vector control mechanism. Nevertheless, the study of this paper shows that there is a limitation in the conventional standard vector control method. Some of the limitations have also been found recently by other researchers. To overcome the shortage of the conventional vector control technique, this paper proposes a new direct-current d–q vector control mechanism in a nested-loop control structure, based on which an optimal control strategy is developed in a nonlinear programming formulation. The behaviors of both the conventional and proposed control methods are compared and evaluated in simulation and laboratory hardware experiment environments, both of which demonstrates that the proposed approach is effective for grid-connected power converter control in a wide system conditions while the conventional standard vector control approach may behave improperly especially when the converter operates beyond its PWM saturation limit.
We discuss a variety of adaptive critic designs (ACDs) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: heuristic dynamic programming, dual heuristic programming, and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACDs. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACDs. This leads to a generalized training procedure for ACDs.
After analyzing the basic principle of vector control in permanent magnet synchronous motor (PMSM) drive, this paper proposes a novel artificial neural network (ANN) based vector control. Here, ANN is used in speed control and space vector pulse width modulation (SVM), since an artificial neural network based speed controller does not rely on the accurate mathematical model of system, and it has not only fast dynamic response but also high steady-state accuracy, while an artificial neural network based SVM (ANN-SVM) algorithm can be realized easily with a small amount of calculation and efficiently-reduced current harmonic. A PMSM drive simulation model with ANN based vector control is created and studied using Matlab/Simulink. The simulation results demonstrate the feasibility and validity of ANN based vector control.
This paper proposes the practical implementation of a fuzzy logic
control scheme applied to a two phase (d-q) current model of an
induction motor. Most conventional fuzzy controllers use fuzzy logic to
tune a PI or PID. The fuzzy logic control scheme does not depend on the
conventional PI or PID model. It directly transforms the inputs, speed
errors and rate of change in speed errors, into the output quantity, the
change in inverter command frequency. The proposed approach is simpler
in implementation and its trajectory tracking capability was
investigated. The experimental results show that it has a superior
performance compared to a fuzzy tuned PI controller or a simple PID
controller
This paper presents the practical implementation of a computer
based slip frequency vector control scheme of an induction motor. A
recurrent artificial neural network is used to transform the input
parameters, speed error and rate of change of speed error, into an
output quantity, the change in inverter output frequency. The recurrent
neural network used has an output layer, an input layer and no hidden
layer. A novel learning algorithm called the vector space searching
algorithm is used to update the network weight. The control algorithm
was coded in C++ and implemented on a 486 personal computer
We investigate the use of an `adaptive critic' based controller to
steer an agile missile with a constraint on the angle of attack from
various initial Mach numbers to a given final Mach number in minimum
time while completely reversing its flightpath angle. We use neural
networks with a two-network structure called `adaptive critic' to carry
out the optimization process. This structure obtains an optimal
controller through solving Hamiltonian equations. This approach needs no
external training; each network along with the optimality equations
generates the output for the other network. When the outputs are
mutually consistent, the controller output is optimal. Though the
networks are trained off-line, the resulting control is a feedback
control
A learning algorithm for multilayer feedforward networks, RPROP
(resilient propagation), is proposed. To overcome the inherent
disadvantages of pure gradient-descent, RPROP performs a local
adaptation of the weight-updates according to the behavior of the error
function. Contrary to other adaptive techniques, the effect of the RPROP
adaptation process is not blurred by the unforeseeable influence of the
size of the derivative, but only dependent on the temporal behavior of
its sign. This leads to an efficient and transparent adaptation process.
The capabilities of RPROP are shown in comparison to other adaptive
techniques
In this correspondence, adaptive critic approximate dynamic programming designs are derived to solve the discrete-time zero-sum game in which the state and action spaces are continuous. This results in a forward-in-time reinforcement learning algorithm that converges to the Nash equilibrium of the corresponding zero-sum game. The results in this correspondence can be thought of as a way to solve the Riccati equation of the well-known discrete-time H<sub>infin</sub> optimal control problem forward in time. Two schemes are presented, namely: 1) a heuristic dynamic programming and 2) a dual-heuristic dynamic programming, to solve for the value function and the costate of the game, respectively. An H<sub>infin</sub> autopilot design for an F-16 aircraft is presented to illustrate the results
In this paper, a new predictive current controller for a permanent magnet synchronous motor (PMSM) considering delays is presented. In a full digital current control system for a PMSM, there are inevitable delays in calculating and applying the inverter output voltages to the motor terminals. A predictive current controller implemented in a full digital system has serious problems such as the oscillation and large overshoot. A discussion of compensation methods to cope with the nonlinearities of the real system is also presented. The proposed current controller has been analyzed, and the experimental results are shown to prove the feasibility and effectiveness of the proposed predictive current controller using a prototype 750 W PMSM servo drive system.
This paper investigates the performance of an interior permanent
magnet synchronous motor (IPMSM) drive over wide speed range for high
precision industrial applications. The scheme incorporates the maximum
torque per ampere (MTPA) operation in constant torque region and the
flux-weakening operation in constant power region in order to expand the
operating limits for an IPMSM. Improved mathematical expressions are
derived to analyze the performances of the IPMSM. The power ratings of
the motor and the inverter are considered. The effects of motor
parameters particularly, the saliency ratio
(X<sub>q</sub>/X<sub>d</sub>) on the voltage limit constraint and the
power capability of the inverter are also investigated. The efficacy of
the above mentioned drive system and the improved steady-state analysis
are evaluated by both experimental and computer simulation results. The
complete drive is implemented in real-time using digital signal
processor (DSP) controller board DS 1102 on a laboratory 1 hp interior
permanent magnet synchronous motor
This paper investigates the performance of an interior permanent magnet synchronous motor (IPMSM) drive over wide speed range for high-precision industrial applications. The scheme incorporates the maximum torque per ampere (MTPA) operation in constant torque region and the flux-weakening operation in constant power region in order to expand the operating limits for an IPMSM. Improved mathematical expressions are derived to analyze the performances of the IPMSM. The power ratings of the motor and the inverter are considered. The effects of motor parameters particularly, the saliency ratio (Xq/Xd) on the voltage limit constraint, and the power capability of the inverter are also investigated. The efficacy of the above-mentioned drive system and the improved steady-state analysis are evaluated by both experimental and computer simulation results. The complete drive is implemented in real time using digital signal processor (DSP) controller board DS 1102 on a laboratory 1 hp interior permanent magnet synchronous motor.
This paper presents a novel sensorless control strategy for a salient-pole permanent-magnet synchronous motor (PMSM). A new model of a salient-pole PMSM using an extended electromotive force (EMF) in the rotating reference frame is utilized to estimate both position and speed. The extended EMF is estimated by a least-order observer, and the estimation position error is obtained from the extended EMF. Both estimated position and speed are corrected so that the position error becomes zero. The proposed system is very simple and the design procedure is easy and clear. Several experimental drive tests are demonstrated and the experimental results show the effectiveness of the proposed sensorless control system.
In a world where environment protection and energy conservation
are growing concerns, the development of electric vehicles (EV) and
hybrid electric vehicles (HEV) has taken on an accelerated pace. The
dream of having commercially viable EVs and HEVs is becoming a reality.
EVs and HEVs are gradually available in the market. This paper will
provide an overview of the present status of electric and hybrid
vehicles worldwide and their state of the art, with emphasis on the
engineering philosophy and key technologies. The importance of the
integration of technologies of automobile, electric motor drive,
electronics, energy storage, and controls and also the importance of the
integration of society strength from government, industry, research
institutions, electric power utilities, and transportation authorities
are addressed. The challenge of EV commercialization is discussed
A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforseeable influence of the size of the derivative but only dependent on the temporal behaviour of its sign. This leads to an efficient and transparent adaptation process. The promising capabilities of RPROP are shown in comparison to other wellknown adaptive techniques. I.
Energy Management and Control of Electrical Drives in Hybrid Electrical Vehicles
- J Ottosson
J. Ottosson, "Energy Management and Control of Electrical Drives in Hybrid Electrical Vehicles," Department of Industrial Electrical Engineering and Automation, Lund University, Sweden, 2007.
Implementation of a Speed Field Oriented Control of 3-Phase PMSM Motor using TMS320F240
- E Simon
E. Simon, "Implementation of a Speed Field Oriented Control of 3-Phase PMSM Motor using TMS320F240," Application Report, SPRA588, Texas Instruments, 1999.
Advanced Electric Drives – Analysis, Modeling and Control using Simulink, MN: Minnesota Power Electronics Research & Education
- N Mohan
N. Mohan, Advanced Electric Drives – Analysis, Modeling and Control using Simulink, MN: Minnesota Power Electronics Research & Education, ISBN 0-9715292-0-5, 2001.
Hybrid Electric Vehicle (HEV) Power Tran Using Battery Model
- P Brunelle
P. Brunelle, " Hybrid Electric Vehicle (HEV) Power Tran Using Battery Model, " The MathWork, January 2007.