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Fuzzy adaptive optimal fault-tolerant trajectory tracking control for underactuated AUVs with input saturation

OCEAN ENGINEERING 2024年第Part2期311卷

作者： Gong, Huibin Er, Meng Joo Liu, Yi Dalian Maritime Univ Inst Artificial Intelligence & Marine Robot Coll Marine Elect Engn Dalian 116026 Peoples R China

Control of underactuated autonomous underwater vehicles (AUVs) not only needs to consider fault-tolerance, but also pursues optimal performance. To this end, a fuzzy adaptive optimal fault-tolerant trajectory tracking control scheme is proposed, which realizes three-dimensional optimal trajectory tracking control of underactuated AUVs with actuator faults and constraints, dynamic uncertainties, and environmental disturbances. The features of the scheme are: (1) System outputs are redefined by a coordinate transformation to handle the underactuation problem, and an asymmetric smooth saturation model is designed to constrain control inputs. (2) An adaptive disturbance observer is constructed to estimate compound disturbances consisting of actuator faults, uncertain dynamics, disturbances, and saturation compensation values. (3) A backstepping controller is designed to transform the original system into an equivalent tracking error dynamic system with an affine nonlinear form while compensating for compound disturbances. (4) A generalized fuzzy hyperbolic model and adaptive dynamic programming are utilized to establish an optimal controller to optimize control energy and tracking errors. Through analysis, all signals of the closed-loop system are uniformly ultimately bounded, and tracking errors can converge to arbitrarily small intervals. Pure software simulation and hardware-in-the-loop test results demonstrate the advantages and feasibility of the proposed scheme.

关键词： Underactuated autonomous underwater vehicles Trajectory tracking control Fault-tolerant control Input saturation adaptive dynamic programming Generalized fuzzy hyperbolic model

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Event-triggered control for input-constrained nonzero-sum games through particle swarm optimized neural networks

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NEURAL NETWORKS 2025年 188卷 107430页

作者： Wu, Qiuye Zhao, Bo Liu, Derong Guangdong Police Coll Sch Publ Secur & Traff Management Guangzhou 510230 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

To accommodate the increasing system scale, improve the system operation success rate and save the computational and communication resources, it is urgent to obtain the Nash equilibrium solution for systems with increasing controllers in an effective way. In this paper, nonzero-sum game problem of partially unknown nonlinear systems with input constraints is solved via the particle swarm optimized neural network-based integral reinforcement learning. By introducing the integral reinforcement learning technique, the drift dynamics is not required any more. To further improve the success rate of system operation, extended adaptive particle swarm optimization algorithm which shares the individual historical optimal position with the whole population is adopted in tuning neural network weights, rather than sharing only the current particle in the traditional particle swarm optimization algorithm. The control policy for each player is obtained by solving the coupled Hamilton-Jacobi equation with a single critic neural network, which simplifies the control structure and reduces the computational burden. Moreover, by introducing the event-triggering mechanism, the control policies are updated at event-triggering instants only. Thus, the computational and communication burdens are further reduced. The stability of the closed-loop system is guaranteed by implementing the integral reinforcement learning-based event-triggered control policies via the Lyapunov's direct method. From the comparative simulation results, the developed integral reinforcement learning-based event-triggered control scheme via the extended adaptive particle swarm optimization performs better than those using gradient descent algorithm, nonlinear programming, particle swarm optimization and other popular training algorithms.

关键词： adaptive dynamic programming Integral reinforcement learning Event-triggering mechanism Particle swarm optimization Neural networks Nonzero-sum games

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Neural-network-based accelerated safe Q-learning for optimal control of discrete-time nonlinear systems with state constraints☆

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NEURAL NETWORKS 2025年 186卷 107249页

作者： Zhao, Mingming Wang, Ding Qiao, Junfei Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

For unknown nonlinear systems with state constraints, it is difficult to achieve the safe optimal control by using Q-learning methods based on traditional quadratic utility functions. To solve this problem, this article proposes an accelerated safe Q-learning (SQL) technique that addresses the concurrent requirements of safety and optimality for discrete-time nonlinear systems within an integrated framework. First, an adjustable control barrier function is designed and integrated into the cost function, aiming to facilitate the transformation of constrained optimal control problems into unconstrained cases. The augmented cost function is closely linked to the next state, enabling quicker deviation of the state from constraint boundaries. Second, leveraging offline data that adheres to safety constraints, we introduce an off-policy value iteration SQL approach for searching a safe optimal policy, thus mitigating the risk of unsafe interactions that may result from suboptimal iterative policies. Third, the vast amounts of offline data and the complex augmented cost function can hinder the learning speed of the algorithm. To address this issue, we integrate historical iteration information into the current iteration step to accelerate policy evaluation, and introduce the Nesterov Momentum technique to expedite policy improvement. Additionally, the theoretical analysis demonstrates the convergence, optimality, and safety of the SQL algorithm. Finally, under the influence of different parameters, simulation outcomes of two nonlinear systems with state constraints reveal the efficacy and advantages of the accelerated SQL approach. The proposed method requires fewer iterations while enabling the system state to converge to the equilibrium point more rapidly.

关键词： Accelerated value iteration adaptive dynamic programming Control barrier functions Neural networks State constraints Safe Q-learning

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adaptive actor-critic network-based appointed-time attitude stabilization under actuator faults and dual-state constraints

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 160卷

作者： Chen, Zhongbo Yang, Xuebo Dong, Hanlin Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China

In this article, adaptive dynamic programming (ADP)-based attitude stabilization is achieved in the presence of external disturbances, actuator failures, and kinematic constraints. First, we design a new appointed-time convergence performance constraint function to constrain the transient and steady-state performance of attitude and angular velocity. Next, the attitude control system with state constraints is transformed into an unconstrained system through state mapping. Then, combining the extended state observer (ESO) and the integral sliding mode (ISM), an adaptive integral sliding mode controller is designed to eliminate disturbances and actuator failures to ensure that the system can move toward the nominal surface. Finally, the actor-critic network employs the adaptive weight update law to approximate the optimal value function and the optimal controller of the nominal system, respectively, without relying on the persistent excitation (PE) condition. The uniformly ultimately bounded (UUB) stability of the nominal system is obtained via the Lyapunov method. Moreover, it can be found from the numerical simulation that under the proposed control scheme, the attitude control system's attitude angle and attitude angular velocity both meet the performance constraints, and the cost consumption is much lower than other control schemes.

关键词： adaptive dynamic programming Prescribed performance control Spacecraft attitude control Sliding mode control Fault-tolerant control

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Sliding Mode-based Integral Reinforcement Learning Event Triggered Control

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第1期23卷 315-331页

作者： Jia, Chao Li, Xinyu Wang, Hongkun Song, Zijian Tianjin Univ Technol Sch Elect Engn & Automat Tianjin Peoples R China

For a class of continuous-time nonlinear systems with input constraints, a novel event triggered control (ETC) of integral reinforcement learning (IRL) based on sliding mode (SM) is proposed in this paper. Firstly, a SM surface-based performance index function is designed and the Hamiltonian equation is solved by the policy iteration algorithm. Secondly, the IRL technique is utilized to obtain the integral Bellman equation, which makes the controller do not need to know the drift dynamics. Thirdly, the ETC is introduced to reduce the communication burden and a triggering condition is designed to ensure the asymptotic stability of the system. Then, a critic neural network (NN) is used to learn the optimal value function to obtain the optimal tracking controller. Finally, the asymptotic stability of the whole closed-loop system and uniformly ultimately bounded of the critic NN weights are proved based on the Lyapunov theory. Simulation and comparison results demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming event-triggered control integral reinforcement learning sliding mode

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Robust Optimal Consensus Control for Nonlinear Multi-agent Systems: Two Hybrid dynamic Event-Triggered-Based Approach

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2025年第2期39卷 398-423页

作者： Zou, Haoming Zhang, Guoshan Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

In this paper, the dynamic event-triggered robust optimal consensus control problem is investigated for nonlinear multi-agent systems (MASs) with matched and mismatched disturbances via integral sliding mode (ISM) control and the adaptive dynamic programming (ADP) algorithm. First, a dynamic event-triggered fixed-time ISM controller is designed to guarantee that the system state converges to the predefined ISM manifold, thereby eliminating the matched disturbances. Next, the consensus control problem is transformed into an optimal control problem by constructing neighborhood error dynamics and a new modified cost function;thus, an event-triggered-based coupled Hamilton-Jacobi-Bellman equation (HJBE) is established. Then, an ADP-based single-critic neural network (NN) is constructed to solve coupled HJBE to obtain the event-triggered optimal controller, in which the NN weight is updated only at the triggering instants. Through the implementation of these two dynamic event-triggered mechanisms, resources and controller execution time can be saved. It is proved that the whole closed-loop system signals are uniformly ultimately bounded by the Lyapunov technique. Finally, two illustrative examples verify the effectiveness and superiority of the proposed control scheme.

关键词： adaptive dynamic programming dynamic event-triggered mechanisms integral sliding mode control robust consensus control

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Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

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INTERNATIONAL JOURNAL OF CONTROL 2013年第9期86卷 1554-1566页

作者： Liu, Derong Huang, Yuzhu Wang, Ding Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： nonlinear observer adaptive dynamic programming neural network uniformly ultimately bounded nonlinear system

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Improved cost function-based fault tolerant control for nonlinear systems with simultaneous faults

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NEUROCOMPUTING 2025年 638卷

作者： Zeng, Chujian Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

This paper develops a fault-tolerant control (FTC) approach for nonlinear systems with simultaneous actuator and sensor faults by utilizing the adaptive dynamic programming technique. The developed approach integrates a descriptor observer with a neural network (NN) fault estimator, which enables simultaneous and precise estimations of system states and faults. By incorporating the estimated actuator fault information into design of an improved cost function, the FTC problem can be transformed into a general optimal control problem. The Hamilton-Jacobi-Bellman equation for the nominal system (the fault-free system) is solved by constructing a critic NN. Thus, an approximate optimal control law is obtained to mitigate the impact of actuator faults. The faulty signal is replaced by the accurate estimation of the system states, thereby mitigating the influence of sensor faults. It is proven that the developed approach ensures the uniform ultimate boundedness of the closed-loop faulty system. Simulation studies are presented to illustrate the effectiveness of the developed approach.

关键词： adaptive dynamic programming Fault tolerant control Neural networks Simultaneous faults Descriptor observer

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Event-triggered explorized IRL-based decentralized fault-tolerant guaranteed cost control for interconnected systems against actuator failures

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NEUROCOMPUTING 2025年 615卷

作者： Liang, Yuling Luo, Yanhong Su, Hanguang Zhang, Xiaoling Chang, Hongbin Zhang, Jun Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Liaoning Peoples R China Northeastern Univ Sch Informat Sci & Engineer Shenyang 110809 Liaoning Peoples R China

This paper presents a novel data-based decentralized guaranteed cost (DGC) fault tolerant control (FTC) scheme for the large-scale systems subject to actuator faults and mismatched interconnection. First, the FTC issues of interconnected systems are converted into a series of near optimal event-triggered control (ETC) methods for isolated subsystems via constructing a modified performance index function of each subsystem. By means of adaptive dynamic programming (ADP) algorithm, the upper bound of performance index function of large-scale systems can be obtained by solving the Hamilton-Jacobi-Bellman (HJB) equation of each auxiliary subsystem. Second, according to the proposed ADP-based decentralized approach and utilizing the event-based synchronous integral reinforcement learning (IRL) algorithm, a model-free guaranteed cost (GC) FTC approach is developed for interconnected large-scale system which can relax the restriction on the condition that system functions must be known. Further, the ultimate uniformly bounded (UUB) stability of auxiliary subsystems can be proved according to the Lyapunov principle. Finally, the effectiveness of the proposed control method is verified by presenting the simulation results.

关键词： Fault tolerant control Interconnected systems Decentralized control adaptive dynamic programming

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Discounted Stable adaptive Critic Design for Zero-Sum Games With Application Verifications

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11706-11716页

作者： Ren, Jin Wang, Ding Li, Menghua Qiao, Junfei Beijing Univ Technol Sch Informat Sci & Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

In this paper, an adaptive critic design with performance guarantee is established based on the discounted value iteration algorithm to settle with the optimal regulation problem for discrete-time zero-sum games. Value iteration is implemented to obtain the approximate optimal solutions to the Hamilton-Jacobi-Isaacs equation for nonlinear systems and the game algebraic Riccati equation for linear systems. Then, we focus on system stability affected by the introduction of the discount factor and the admissibility of the policy pairs in the value iteration process. The appropriate selection range of the discount factor and the criteria for ensuring system stability are established to assist in obtaining the stabilized optimal policy pair, which not only makes the cost function converge to the optimal value, but also guarantees the asymptotic stability of the closed-loop system. Finally, practical examples for the power system and the ball-beam system are conducted to demonstrate the effectiveness of the presented method.

关键词： Games Game theory Stability criteria Asymptotic stability Optimal control Power system stability dynamic programming Heuristic algorithms Cost function Nonlinear systems adaptive critic designs adaptive dynamic programming discounted value iteration stability zero-sum games

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