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An adaptive neural controller based on neural emulator for single-input multi-output nonlinear systems
Abstract
This paper deals with the adaptive control of single-input multi-output (SIMO) underactuated nonlinear systems. The restriction of the control authority for these systems causes major difficulties in control design. In this work, we propose an adaptive neural controller based on neural emulator to solve the control problems for a class of SIMO nonlinear systems. This controller is built by a set of partial neural controllers. New formulas are proposed to compute the validity degrees which manage the generation of the global control law. Simulations are carried out on a single-input multi-output nonlinear system. The obtained results show that the suggested control scheme provides a very good tracking and regulation performance.
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A hierarchical sliding mode control approach is pro-posed for a class of SIMO under-actuated systems. This class of under-actuated systems is made up of several subsystems. Based on this physical structure, the hierarchical structure of the sliding surfaces is designed as follows. At first, the sliding surface of every subsystem is defined. Then the sliding surface of one subsystem is defined as the first layer sliding surface. The first layer sliding sur-face is used to construct the second layer sliding surface with the sliding surface of another subsystem. This process continues till the sliding surfaces of the entire subsystems are included. According to the hierarchical structure, the total control law is deduced by the Lyapunov theorem. In theory, the asymptotic stability of the entire system of sliding surfaces is proven and the parameter boundaries of the subsystem sliding surfaces are given. Simulation results show the feasibility of this control method through two typical SIMO under-actuated systems.
In this paper, a real time recurrent learning-based emulator is presented for nonlinear plants with unknown dynamics. This emulator is based on fully connected recurrent neural networks. Starting from zero values, updating rate, time parameter and weights of the instantaneous neural emulator adapt themselves in order to estimate the process output. The contribution of this paper is to validate the emulator with experimental data from the batch reactor of National Engineering School of Gabes, Tunisia.
This paper is concerned with the inherent H2 tracking performance limitation of single-input and multiple-output (SIMO) linear time-invariant (LTI) feedback control systems. The performance is measured by the tracking error between a step reference input and the plant output with additional penalty on control input. We employ the plant augmentation strategy, which enables us to derive analytical closed-form expressions of the best achievable performance not only for discrete-time system, but also for continuous-time system by exploiting the delta domain version of the expressions.
In this paper, we address the tracking problem for an underactuated ship using two controls, namely surge force and yaw moment. A simple state-feedback control law is developed and proved to render the tracking error dynamics globally K- exponentially stable. Experimental results are presented where the controller is implemented on a scale model of an offshore supply vessel.
The paper deals with the automatic control of a dam river system,
where the action variable is the upstream discharge and the controlled
variable the downstream discharge. The system is a cascade of single
input-single output (SISO) systems, and can be considered as a single
input-multiple output (SIMO) system, since there are multiple outputs
given by intermediate measurement points distributed along the river. A
generic robust design synthesis based on internal model controller (IMC)
design is developed for internal model based controllers. The robustness
is estimated with the use of a bound on multiplicative uncertainty
taking into account the model errors, due to the nonlinear dynamics of
the system. Simulations are carried out on a nonlinear model of the
river
In this paper, multimodel and neural emulators are proposed for nonlinear plants with unknown dynamics. The contribution of this paper is to extend the emulators to multi-variable non square systems. The effectiveness of the proposed emulators are shown through an illustrative simulation example. The obtained results are very satisfactory and show the best performances of the multimodel emulator relatively to the neural one.
This work investigates an uncoupled multimodel emulator for non-linear system control design. Efficiency of the proposed multimodel emulator is illustrated by comparison with the neural one by their application to SISO indirect adaptive neural control. Neither an initialisation parameter and nor online adaptation is required for multimodel emulator unlike the neural one. The effectiveness of the proposed multimodel emulator is shown through numerical simulations. The obtained results using multimodel emulator are far better than neural emulator.
This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this paper are the determination of Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller and the development of new adaptation strategies based on the tracking error dynamics and Lyapunov stability analysis to improve the closed loop performances. Efficiency of the proposed controller is illustrated with nonlinear system simulations. An application of the developed approaches to a hot-air blower is presented in order to validate simulations results.
In this paper, we investigate the regulation properties pertaining to single-input multiple-output (SIMO) linear time-invariant (LTI) systems, in which the objective function of regulated response is minimized jointly with the control effort. We provide analytical closed-form expressions of the best achievable H2 optimal regulation performances against impulsive disturbance inputs for unstable/non-minimum phase continuous-time and discrete-time systems. We also modify the latter results by means of the delta operator and show the continuity property, i.e., we show that the continuous-time solution can be completely recovered when the sampling period tends to zero in the delta domain solution. We then apply the results to a magnetic bearing system and discuss relations between the sensor selection and the best achievable H2 performances.
This paper deals with the tracking control problem of a manipulator system with unknown and changing dynamics. In this study, a fuzzy logic controller (FLC) in the feedback configuration and an efficient dynamic recurrent neural network (DRNN) in the feedforward configuration are proposed. The DRNN, which possesses the ability of approaching arbitrary nonlinear function, is applied to approximate the inverse dynamics of the robotic manipulator system. Based on the outputs of the FLC, parameter updating equations are derived for the adaptive DRNN model. The analysis of the stability of the system is also carried out. Finally, extensive simulations are conducted under different conditions. Results demonstrate the remarkable performance of the proposed controller. It can successfully identify the inverse dynamics of the flexible manipulator system and perform accurate tracking for a given trajectory.
AbstractA novel adaptive dual network design consisting of a rough adjustment network (RAN) and a fine adjustment network (FAN) is proposed to eliminate the unknown time-variant uncertainties of servo system. To accomplish this objective, a RAN is proposed based on the combination of sliding mode control, function approximation, and error compensation technique. Then, an FAN is proposed to compensate the tracking error. In our current design, the FAN includes a critic network based on a neural network model and a prediction network based on an online curve fitting scheme. Theoretical analysis followed by detailed design strategies are presented in this work. Simulation results and comparative study of this method with those of existing approaches demonstrate the effectiveness of the proposed adaptive dual network design for position tracking.
Two novel compensation schemes based on accelerometer measurements to attenuate the effect of external vibrations on mechanical systems are proposed in this paper. The first compensation algorithm exploits the neural network as the feedback-feedforward compensator whereas the second is the neural network feedforward compensator. Each compensation strategy includes a feedback controller and a neural network compensator with the help of a sensor to detect external vibrations. The feedback controller is employed to guarantee the stability of the mechanical systems, while the neural network is used to provide the required compensation input for trajectory tracking. Dynamics knowledge of the plant, disturbances and the sensor is not required. The stability of the proposed schemes is analyzed by the Lyapunov criterion. Simulation results show that the proposed controllers perform well for a hard disk drive system and a two-link manipulator.
In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference operator, the nonlinear terms of the system are not required to be globally bounded. The proposed adaptive control scheme is composed of a linear adaptive controller, a neural-network-based nonlinear adaptive controller and a switching mechanism. The linear controller can assure boundedness of the input and output signals, and the neural network nonlinear controller can improve performance of the system. By using the switching scheme between the linear and nonlinear controllers, it is demonstrated that improved performance and stability can be achieved simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.
The purpose of this paper is to present a simple neuro-control law in order to control a geared DC motor. The main advantage of this controller is that it does not require an exact knowledge of the values of the motor parameters. The proposed artificial neural network is characterized by two input synaptic weights, two output synaptic weights and one threshold; these parameters are used to define the performance of the closed loop system. The DC motor parameters, the synaptic weights and the ANN threshold are combined in order to construct an off-line learning condition. Such condition guarantees that the seminorm of the regulation error remains bounded (closed loop performance index) and it is constructed through a Lyapunov-like analysis. The neuro-controller is evaluated through numerical simulations and through small-scale laboratory experiments by implementing the neuro-controller with electronic hardware.
In this paper, stable indirect adaptive control with recurrent neural networks is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. The control scheme is then applied to the Tennessee Eastman Challenge Process in order to illustrate the efficiency of the proposed method for real-world control problems.
Underactuated mechanical systems are those possessing fewer
actuators than degrees of freedom. Examples of such systems abound,
including flexible joint and flexible link robots, space robots, mobile
robots, and robot models that include actuator dynamics and rigid body
dynamics together. Complex internal dynamics, nonholonomic behavior, and
lack of feedback linearizability are often exhibited by such systems,
making the class a rich one from a control standpoint. In this article
the author studies a particular underactuated system known as the
Acrobot: a two-degree-of-freedom planar robot with a single actuator.
The author considers the so-called swing up control problem using the
method of partial feedback linearization. The author gives conditions
under which the response of either degree of freedom may be globally
decoupled from the response of the other and linearized. This result can
be used as a starting point to design swing up control algorithms.
Analysis of the resulting zero dynamics as well as analysis of the
energy of the system provides an understanding of the swing up
algorithms. Simulation results are presented showing the swing up motion
resulting from partial feedback linearization designs
We introduce cascade normal forms for underactuated mechanical
systems that are convenient for control design. These normal forms
include three classes of cascade systems, namely, nonlinear systems in
strict feedback form, feedforward form, and nontriangular quadratic form
(to be defined). In each case, the transformation to cascade systems is
provided in closed-form. We apply our results to the Acrobot, the
rotating pendulum, and the cart-pole system
Optimal tracking performance for SIMO systems
- G Chen
- J Chen
- R H Middleton