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Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC
... Compared to the finite-time control methods presented in [25][26][27][28][29], a novel finite-time control law based on the hyperbolic tangent function is developed; it ensures the path-following errors converge to a bounded region around the origin within a finite time. In addition, the proposed finite-time control law can better deal with the effect of time-varying disturbances, also providing smoother control inputs. ...
This paper presents a three-dimensional (3D) robust adaptive finite-time path-following controller for underactuated Autonomous Underwater Vehicles (AUVs), addressing model uncertainties, external disturbances, and actuator magnitude and rate saturations. A path-following error system is built in a path frame using the virtual guidance method. The proposed cascaded closed-loop control scheme can be described in two separate steps: (1) A kinematic law based on a finite-time backstepping control (FTBSC) is introduced to transform the 3D path-following position errors into the command velocities; (2) The actual control inputs are designed in the dynamic controller using an adaptive fixed-time disturbance observer (AFTDO)-based FTBSC to stabilize the velocity tracking errors. Moreover, the adverse effects of magnitude and rate saturations are reduced by an auxiliary compensation system. A Lyapunov-based stability analysis proves that the path-following errors converge to an arbitrarily small region around zero within a finite time. Comparative simulations illustrate the effectiveness and robustness of the proposed controller.
The permanent magnet synchronous motor (PMSM) has been of interest to eco-friendly industries on account of its advantages such as high performance, efficiency, and precision control. However, perturbations due to PMSM parameter uncertainty, load torque, and external disturbance interfere with the construction of PMSM precision control systems. Therefore, a robust control system is needed to avoid unnecessary system movement caused by perturbations. In this paper, sliding mode control (SMC) is adopted to implement a robust control system for the PMSM. In order to reduce the reaching time from the initial system state to the sliding surface and the chattering phenomenon that can cause the system to malfunction, the adaptive quick sliding mode reaching law based on an exponential function and power equation is proposed. Although the SMC is robust to disturbance and parameter uncertainty, unexpected disturbances can destabilize the system. To estimate the unmatched disturbance in a short time, the second-order fast terminal sliding mode observer (SFTSMO) is proposed. The results show that the motor control system based on the proposed method has a fast convergence speed to an objective value, position tracking performance, and robustness.
A sliding mode control (SMC) method is designed to attenuate the disturbances in the DC–DC buck converter system based on the estimated values of extended state observer (ESO). A conventional ESO is unable to accurately estimate system states and disturbances in finite time limited by its linear nature. This weakness cannot ensure that the system reaches the nominal sliding mode surface within finite time, thus the sliding mode trajectory will be affected. Therefore, a novel finite-time extended state observer (FTESO) is presented, utilizing a homogeneous theorem for finite-time convergence. Subsequently, a FTESO-based nonsingular terminal sliding mode control (NTSMC) method is devised for achieving globally finite-time stable of the closed-loop system. The Lyapunov stability theorem and homogeneous theorem are used to prove the finite-time stability, and the upper bound of settling time is estimated explicitly. Finally, both numerical simulations and experiments demonstrate the superiority of the designed control scheme with modeling errors and load disturbances.
This article addresses a trajectory tracking control problem concerning an autonomous underwater vehicle's pitch and yaw channel dynamics in the presence of model uncertainties, underwater disturbances, and input saturation. Three different observers are introduced to estimate unknown state variables: a Luenberger‐type cubic observer, a sliding mode observer, and a high‐gain observer (HGO). Initially, a backstepping controller is employed to tackle the tracking problem, extending it to incorporate backstepping sliding mode control (SMC). The mentioned observers are utilized in both aspects of the controller design. Our proposed control law assesses trajectory tracking performance by introducing virtual control inputs, with the sliding surface designed to guide the current state variables toward approximating the virtual state variables. By combining backstepping and SMC, ensure that the state variables of the closed‐loop system converge to the desired state using the HGO. A rigorous analysis is incorporated to validate the robust performance of our proposed control law under conditions of model uncertainties and underwater disturbances. Furthermore, the control law is extended for anti‐windup compensation, mitigating adverse effects on stern and rudder plane saturation levels. Lyapunov stability theory is adopted to establish the stability of the closed‐loop system. Our simulation results convincingly demonstrate the effectiveness of the HGO‐based backstepping SMC law compared to alternative control approaches.
The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified.
Offshore aquaculture fish farming faces labor shortage, safety, productivity and high operating cost issues. Unmanned underwater vehicles (UUVs) are being deployed to mitigate these issues. One of their applications is the fish net-pen visual inspection. This paper aims to develop and simulate with high-fidelity several trajectory tracking control schemes for a UUV to visually inspect a fish net-pen in a standard task scenario in offshore aquaculture under 0.0m/s, 0.5m/s and 0.9m/s underwater current disturbances. Three controllers, namely (1) Proportional-Derivative control with restoring force & moment compensation (Compensated-PD), (2) Proportional-Integral-Derivative control with restoring force & moment compensation (Compensated-PID), and (3) computed torque (or) inverse dynamics control (CTC/IDC) were conducted on a 6 degrees-of-freedom (DoF) BlueROV2 Heavy Configuration dealing with 12 error states (pose and twist). A standard task scenario for the controllers was formulated based on the Blue Endeavour project of the New Zealand King Salmon company located 5 kilometres due north of Cape Lambert, in northern Marlborough. This simulated experimental study gathered and applied many available and physically quantifiable parameters of the fish farm and a UUV called BlueROV2 Heavy Configuration. Results show that while utilizing the minimum thrust, CTC/IDC outperforms Compensated-PID and Compensated-PD in overall trajectory tracking under different underwater current disturbances. Numerical results measured with root-mean-square-error (RMSE), mean-absolute-error (MAE) and root-sum-squared (RSS) are reported for comparison, and simulation results in the form of histograms, bar charts, plots, and video recordings are provided. Future work will explore into advanced controllers, with a specific emphasis on energy-optimal control schemes, accompanied by comprehensive stability and robustness analyses applied to linear and nonlinear UUV models.
Unmanned underwater vehicles (UUVs) have become increasingly popular in recent years due to their use in various applications. The motivations for using UUVs include the exploration of difficult and dangerous underwater environments, military tasks in mine detection, intelligence gathering and surveillance, the inspection of offshore oil and gas infrastructure in the oil and gas industry, scientific research for studying marine life, and the search and rescue of missing persons or submerged airplanes or boats in underwater environments. UUVs offer many advantages in achieving the desired applications with increased safety, efficiency, and cost-effectiveness. However, there are also several challenges associated with their communication, navigation, power requirements, maintenance, and payload limitations. These types of vehicles are also prone to various disturbances caused by currents of the ocean, propulsion systems, and unmolded uncertainties. Practically, it is a challenging task to design a controller that will ensure optimal performance under these conditions. Therefore, the control system design is of prime importance in the overall development of UUVs. Also, the UUV controller receives input from different sensors, and the data from these sensors are used by the controller to perform different tasks. The control systems of UUVs should take into account all uncertainties and make them stable so that all sensors can perform optimally. This paper presents a complete review of different control system design algorithms for UUVs. The basic logic designs of several control system algorithms are also presented. A comparison is made based on reliability, robustness, precession, and the ability of the controller to handle the nonlinearity that is faced by UUVs during their missions. Simulation and experimental results are thoroughly studied to gain insight into each algorithm. The advantages and disadvantages of each algorithm are also presented, which will facilitate the selection of a suitable algorithm for the control system design of UUVs.
This paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC).
Unmanned underwater vehicles (UUVs) are becoming increasingly important for a variety of applications, including ocean exploration, mine detection, and military surveillance. This paper aims to provide a comprehensive examination of the technologies that enable the operation of UUVs. We begin by introducing various types of unmanned vehicles capable of functioning in diverse environments. Subsequently, we delve into the underlying technologies necessary for unmanned vehicles operating in underwater environments. These technologies encompass communication, propulsion, dive systems, control systems, sensing, localization, energy resources, and supply. We also address general technical approaches and research contributions within this domain. Furthermore, we present a comprehensive overview of related work, survey methodologies employed, research inquiries, statistical trends, relevant keywords, and supporting articles that substantiate both broad and specific assertions. Expanding on this, we provide a detailed and coherent explanation of the operational framework of UUVs and their corresponding supporting technologies, with an emphasis on technical descriptions. We then evaluate the existing gaps in the performance of supporting technologies and explore the recent challenges associated with implementing the Thorp model for the distribution of shared resources, specifically in communication and energy domains. We also address the joint design of operations involving unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and UUVs, which necessitate collaborative research endeavors to accomplish mission objectives. This analysis highlights the need for future research efforts in these areas. Finally, we outline several critical research questions that warrant exploration in future studies.
The observation and detection of the subsea environment urgently require large-scale and long-term observation platforms. The design and development of subsea AUVs involve three key points: the subsea-adapted main body structure, agile motion performance that adapts to complex underwater environments, and underwater acoustic communication and positioning technology. This paper discusses the development and evolution of subsea AUVs before proposing solutions to underwater acoustic communication and positioning navigation schemes. It also studies key technologies for the agile motion of subsea AUVs and finally gives an example of a solution for implementing underwater AUVs, i.e., the disk-shaped autonomous underwater helicopter (AUH). This paper will provide guidance for the design of subsea AUVs and the development of corresponding observation and detection technologies.
With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.
This paper addresses the distributed dynamic rendezvous control problem of the AUV-USV heterogeneous joint system in the presence of unknown external disturbances using nonlinear model predictive control. By utilizing the nonlinear model predictive control framework, the rendezvous problem can be solved without constructing explicit error dynamics of the heterogeneous system. The inputs and states constraints such as spatial and velocity constraints can also be considered simultaneously in an intuitive way. To facilitate distributed implementations, distributed optimization problems with incorporated moving suppression are then constructed to guarantee nominal stability. Considering unknown external disturbances, a practical disturbance compensating method is finally proposed, ensuring the satisfaction of state constraints. The nominal recursive feasibility and stability are proved in this paper. Compared with existing works, the proposed scheme improves the applicability and robustness against external disturbances for dynamic rendezvous between two heterogeneous robots. Simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed method.
This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.
Recently problems requiring control unmanned underwater vehicles (UUV) at large angles of inclination (pitch and roll), become more frequent. Traditional attitude control systems use Euler angles. However, the performance of traditional systems decreases with the increasing of the tilt angles, which delays their use for new tasks. To solve this problem, stability analysis of the UUV's attitude control system according to the generalized Nyquist stability criterion is carried out. The analyses showed that the stability of the system depends on the UUV inclination along the roll. However, at large angles of inclination, the roll channel is subject to perturbations from the yaw and pitch channels. The roll control system synthesis is solved as the ∞-optimization problem with the requirements of low sensitivity to perturbations from other channels. The simulation results on the full non-linear UUV Aqua-MO model confirmed the efficiency of the approach in question and demonstrated the best quality in comparison with PD controller. The obtained stability condition and synthesis approach allow to expand the working angles and improve the quality of the existing UUV control systems. These results are useful for the development of new systems as well.
This paper addresses the tracking control problem associated with the diving motion system of a torpedo-like shape autonomous underwater vehicle (AUV). A decoupled and reduced-order three degrees-of-freedom (3-DOF) nonlinear model is employed to represent the dynamics of the diving motion system for depth position control. The control objective is to track the demanded depth position in the presence of uncertainties and disturbances. A control law based on the immersion and invariance (I &I) technique is synthesized to achieve the control objectives. The proposed control law effectively tracks a stable, lower-order target dynamic system immersed within a three-dimensional manifold. Additionally, the regulation problem is treated as a specialized case of tracking, with a known depth serving as the reference input to be regulated. The performance of the proposed control law is assessed through simulation studies that consider various scenarios. The simulation study evaluates the robustness of the proposed control law resilience against modelling uncertainties and underwater disturbances. The simulations utilize the MAYA AUV, incorporating experimentally validated diving motion parameters. The proposed control law’s quantitative analysis and computational performance show better performance against the benchmark controller.
Because of the linear and nonlinear variations in the operating environment, autonomous underwater vehicles (AUVs) are one of the most difficult applications. The complexity of the control algorithm should be less for real-time implementation in a field programable gate array logic (FPGA) device. In this work, a highly accurate FPGA implementation of PID-Fuzzy control strategy is proposed for an AUV operation that is extremely precise. Parameters such as weight, water density, and depth are used to perform highly efficient and accurate control for the proposed system. A type II fuzzy logic controller and accompanying proportional-integral- derivative controller are used to confine pitch and depth boundaries. The proposed design is modeled using SIMULINK software, and Verilog code is generated using hardware description language coder from MATLAB. Xilinx software is used to synthesize the Verilog code for spartan FPGA. The proposed technique improves the accuracy and reduces the response time when compared to the conventional control strategy.
Autonomous underwater vehicles (AUVs) have become important tools in the ocean exploration and have drawn considerable attention. Precise control for AUVs is the prerequisite to effectively execute underwater tasks. However, the classical control methods such as model predictive control (MPC) rely heavily on the dynamics model of the controlled system which is difficult to obtain for AUVs. To address this issue, a new reinforcement learning (RL) framework for AUV path-following control is proposed in this paper. Specifically, we propose a novel actor-model-critic (AMC) architecture integrating a neural network model with the traditional actor-critic architecture. The neural network model is designed to learn the state transition function to explore the spatio-temporal change patterns of the AUV as well as the surrounding environment. Based on the AMC architecture, a RL-based controller agent named ModelPPO is constructed to control the AUV. With the required sailing speed achieved by a traditional proportional-integral (PI) controller, ModelPPO can control the rudder and elevator fins so that the AUV follows the desired path. Finally, a simulation platform is built to evaluate the performance of the proposed method that is compared with MPC and other RL-based methods. The obtained results demonstrate that the proposed method can achieve better performance than other methods, which demonstrate the great potential of the advanced artificial intelligence methods in solving the traditional motion control problems for intelligent vehicles.
Two distinct fast implementation strategies are proposed for the NMPC-based trajectory trackingTrajectory tracking control of an AUVAutonomous underwater vehicle. The first strategy is based on the numerical continuation method. Assuming that the solution of the associated optimization problem is not obtained at singular points, the NMPC control signals can be approximated without undergoing the successive linearization step which is inevitable in off-the-shelf NLP algorithms, therefore, the computational complexity can be significantly reduced. The convergence of the solution is proved. The second strategy exploits the dynamic properties of the AUV motion, and solves the optimization problems in a distributed fashion. The recursive feasibility and closed-loop stability of the distributed implementation are proved. The proposed fast implementation strategies considerably alleviate the heavy computational burden and hence greatly increase the possibility of implementing NMPC-based motion control on various AUVs including those with limited onboard computing resources.
In this paper, a control scheme is designed for the trajectory tracking problem of underactuated autonomous underwater vehicles with input saturation, parameter uncertainty, and disturbance. First, a novel continuous desired heading angle is designed, which is better than traditional tracking error-based desired angle design and excludes the discontinuity problem of the desired angle in traditional design. Second, based on the desired heading angle design, dynamic surface control is introduced, which reduces the computational complexity, and the desired speed can be obtained. A compensation filter is used to compensate for the loss caused by the input saturation of the control signal. Third, novel velocity compensation laws are designed to compensate the sway velocity, which can overcome the challenge of underactuation. Moreover, the parameter uncertainty issue, which widely exists in most engineering systems, can also be handled by the novel velocity compensation laws. In the simulation, the performance of the desired heading angle and velocity compensation law designed in this paper are compared, and simulation results verify the effectiveness of the method proposed in this paper.
In this short essay, we propose a modified adaptive command filtered backstepping control (ACFBC) scheme for AUVs considering model uncertainties and input saturation. Firstly, command filters are introduced not only to solve the computational complexity problem in the traditional backstepping method but also to approximate the un-modeled dynamics in the system. Secondly, a new stability function is constructed to eliminate the influence of an unknown time-varying inertia matrix. Thirdly, an adaptive control scheme is used to evaluate the uncertain time constants in the saturation actuators. Then the uniform ultimate boundedness of all signals in the closed-loop control system is proven. Finally, simulation results are given to show the effectiveness of the proposed control strategy.
This article investigates the robust adaptive control for the longitudinal dynamics of autonomous underwater vehicle with target tracking based on fuzzy logic system. Through dynamic transformation, the target tracking command is converted to the pitch angle command. The tracking controller using switching mechanism is designed where the fuzzy logic system (FLS) cooperates with the robust design to deal with dynamics uncertainty. Considering the approximation ability of FLS, the modeling error is constructed and employed to design the fuzzy update law. The parameter adaptation law is further introduced for the unknown control gain function. The uniformly ultimate boundedness stability is proved through Lyapunov analysis. Simulation tests on the task of moving target tracking present higher tracking and learning performance.
Autonomous underwater vehicles (AUVs) have broad applications owing to their small size, low weight, strong ability to operate autonomously, and ability to replace humans in dangerous operations. AUV motion control systems can ensure stable operation in complex ocean environments and have attracted significant research attention in marine science and technology. The main difficulties with AUV motion control include the large uncertainties of dynamic and hydrodynamic characteristics and time delay of the signal transmission channel. In this study, we propose a robust fractional-order proportional–integral–derivative (FOPID) controller design for an AUV yaw control system. First, a three-dimensional stability region analysis method is proposed to achieve fractional orders. Unlike other stability region analysis methods, the proposed method is supported by theory instead of observation. Then, the other parameters are optimized according to the robust design specifications with respect to the parameter uncertainties. Therefore, the controlled system can tolerate different parameter uncertainties and fulfill transient performance specifications while maintaining system stability. The simulation results illustrate the superior robustness and transient performance of the proposed control algorithm.
This paper investigates the problem of trajectory tracking control for a X rudder autonomous underwater vehicle (AUV) subjects to nonlinearities, uncertainties, and complex actuator dynamics. Under an adaptive energy-efficient tracking control scheme, the trajectory tracking problem is decomposed into kinematics and dynamics control. In kinematics control loop, an improved line-of-sight (LOS) guidance law is proposed with fuzzy-based look-ahead distance optimization mechanism, and feedback kinematics control law is designed utilizing Lyapunov method. The dynamics control loop is divided into two subsystems, namely surge tracking and course tracking. Adaptive chattering-free terminal sliding mode control is employed to improve the tracking performance and converging rate, which introduces fuzzy based parameter optimization method to tackle the chattering problem, and robustness to unknown disturbances is addressed by disturbance observers. The influence of complex actuator dynamics is fully considered, where an energy-efficient rudder allocation method is proposed to deal with the multi-objective optimization problem with multi-constraints, including rudder saturations, rolling restriction, etc., and radial-basis-function neural network (RBFNN) based compensator is utilized to deal with the propeller saturation problem. Finally, plenty of comparative numerical simulations are provided to demonstrate the robustness and effectiveness of the proposed approach.
Aiming at the problem of model parameter uncertainty, unknown current disturbance and actuator input saturation of multi-AUVs formation control, an event-triggered formation strategy based on fixed-time Radial Basis Function(RBF) neural network adaptive disturbance observer is proposed, which can ensure formation control converge in fixed time. First of all, fixed time RBF disturbance observer (FRBFDO) is put forward to estimate lumped disturbance accurately. Based on the FRBFDO, with the combination of command filter and the back-stepping method, which is used to eliminate repeated derivation calculation explosion problem; Secondly, in order to save the energy consumption of network transmission resources, the event trigger mechanism is introduced into the multi-AUVs formation control. The fixed-time distributed formation controller is designed to realize the fixed-time stability of formation system, and the system convergence time is independent of the initial state. Finally, the effectiveness and rationality of the proposed algorithm are proved by the multi-AUVs formation simulation experiment.
This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV). We investigate the nonlinear model predictive control (NMPC) method looking for possible approaches to alleviate the heavy computational burden. Novel distributed NMPC algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately decomposing the original optimization problems into smaller size subproblems and then solving them in a distributed manner, the expected floating point operations (flops) can be reduced dramatically. We show that the convergence of the AUV trajectory can be guaranteed by the proposed contraction constraints in the decomposed subproblems. Recursive feasibility and closed-loop stability are proved. Taking advantage of the guaranteed stability, a real-time distributed implementation algorithm is further developed to automatically trade off between control performance and computational complexity. Extensive simulation studies on the Falcon AUV model demonstrate the effectiveness and robustness of the proposed approach.
This paper presents a novel three-dimension (3-D) underwater trajectory tracking method for an autonomous underwater vehicle (AUV) using model predictive control (MPC). First, the 6-degrees of freedom (DoF) model of a fully-actuated AUV is represented by both kinematics and dynamics. After that, the trajectory tracking control is proposed as an optimization problem and then transformed into a standard convex quadratic programming (QP) problem which can be readily computed online. The practical constraints of the system inputs and states are considered effectively in the design phase of the proposed control strategy. To make the AUV move steadily, the control increments are considered as the system input and optimized. The receding horizon implementation makes the optimal control inputs be recalculated at each sampling instant, which can improve the robustness of the tracking control under the model uncertainties and time-varying disturbances. Simulations are carried out under two different 3-D trajectories to verify the performance of trajectory tracking under random disturbances, ocean current disturbances, and ocean wave disturbances. The simulation results are given to show the feasibility and robustness of the MPC-based underwater trajectory tracking algorithm.
This paper focuses on the trajectory tracking control of unmanned underwater vehicles (UUVs) in the presence of dynamic uncertainties and time-varying external disturbances. Two adaptive integral terminal sliding mode control schemes, namely, adaptive integral terminal sliding mode control (AITSMC) scheme and adaptive fast integral terminal sliding mode control (AFITSMC) scheme are proposed for UUVs based on integral terminal sliding mode (ITSM) and fast ITSM (FITSM), respectively. Each control scheme is double-looped: composed of a kinematic controller and a dynamic controller. First, a kinematic controller is designed for each of the two control schemes. The two kinematic controllers are based on ITSM and FITSM, respectively. These kinematic controllers yield local finite-time convergence of the position tracking errors to zero meanwhile avoid the singularity problem in the conventional terminal sliding mode control (TSMC). Then, using the output of the kinematic controller as a reference velocity command, a dynamic controller is developed for each of the two control schemes. The two dynamic controllers are also based on ITSM and FITSM, respectively. An adaptive mechanism is introduced to estimate the unknown parameters of the upper bound of the lumped system uncertainty which consists of dynamic uncertainties and time-varying external disturbances so that the prior knowledge of the upper bound of the lumped system uncertainty is not required. The estimated parameters are then used as controller parameters to eliminate the effects of the lumped system uncertainty. The convergence rate of the integral terminal sliding variable vector is investigated and the local finite-time convergence of the velocity tracking errors to zero in the ITSM or FITSM is obtained. At last, based on the designed kinematic and dynamic controllers, the finite-time stability of the full closed-loop cascaded system is shown. The two proposed control schemes improve the tracking accuracy over the existing globally finite-time stable tracking control (GFTSTC) and adaptive nonsingular TSMC schemes, and enhance the robustness against parameter uncertainties and external disturbances over the GFTSTC scheme. Compared with the conventional adaptive integral sliding mode control (AISMC) scheme, the two proposed control schemes offer faster convergence rate and stronger robustness against dynamic uncertainties and time-varying external disturbances for the trajectory tracking control of UUVs due to involving the fractional integrator. Comparative numerical simulations are performed on the dynamic model of the Omni Directional Intelligent Navigator UUV for two trajectory tracking cases. The convergence rate and robustness to uncertainties and disturbances are quantified as the convergent time and bounds of the steady-state position and velocity tracking errors, respectively. The results show that the two proposed control schemes improve at least 20s in convergence rate and enhance about 2% robustness in position tracking and 20% robustness in velocity tracking over the AISMC scheme.
This paper studies the trajectory tracking control problem of an autonomous underwater vehicle (AUV). To explicitly deal with the finite thrust capability in the controller design, the Lyapunov-based model predicative control (LMPC) technique is applied. A nonlinear backstepping tracking control law is exploited to construct the contraction constraint in the formulated MPC problem so that the closed-loop stability can be guaranteed. Due to the optimization essential, the thrust allocation (TA) subproblem can be easily incorporated into the LMPC tracking control design. Sufficient conditions that ensure the recursive feasibility hence closed-loop stability are provided analytically. A guaranteed region of attraction (ROA) is explicitly characterized. In the meantime, the robustness of the tracking control can also be improved by the receding horizon implementation that is adopted in the LMPC control algorithm. Simulation results on the Saab SeaEye Falcon model AUV demonstrate the enhanced trajectory tracking control performance via the proposed LMPC.
A continuous finite-time control scheme for rigid robotic manipulators is proposed using a new form of terminal sliding modes. The robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show that faster and high-precision tracking performance is obtained compared with the conventional continuous sliding mode control method.
Application and prospect of deep-sea arv in mineral resource investigation
- Jixiang