Article
Automation & Control Systems
Mingming Liang, Derong Liu
Summary: This article focuses on designing the optimal impulsive controller (IMC) of continuous-time nonlinear systems and proposes a new adaptive dynamic programming algorithm with high generality and feasibility. The introduced policy-improving mechanism makes the algorithm more flexible for memory-limited computing devices.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Mingming Ha, Ding Wang, Derong Liu
Summary: In this paper, a new approach is proposed to address the tracking control problem. By introducing a new cost function and a novel stability analysis method, the issue of incomplete elimination of tracking error in traditional approaches is solved. The specific implementation scheme for the special case of linear systems is also provided.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Computer Science, Artificial Intelligence
Ziyu Lin, Jingliang Duan, Shengbo Eben Li, Haitong Ma, Jie Li, Jianyu Chen, Bo Cheng, Jun Ma
Summary: The research addresses the challenge of solving the finite-horizon HJB equation, proposes a new algorithm, and validates its effectiveness through simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiong Yang, Yuanheng Zhu, Na Dong, Qinglai Wei
Summary: The study focuses on the decentralized event-driven control problem of nonlinear dynamical systems, transforming it into a group of nonlinear optimal control problems and solving it using event-driven Hamilton-Jacobi-Bellman equations to ensure overall system stability. The critic neural network architecture is utilized to address the problem, with Lyapunov method ensuring signal stability in closed-loop subsystems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Qinglai Wei, Tao Li
Summary: This research focuses on optimal control problems (OCPs) with constrained cost for discrete-time nonlinear systems. A novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven, and the method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Linghuan Kong, Shuang Zhang, Xinbo Yu
Summary: An approximate optimal scheme is proposed for an uncertain n-link robot subject to saturation nonlinearity. The proposed method takes into account model uncertainty in robotic dynamics and designs an optimal control under the frame of adaptive dynamic programming. The method is proved to be effective in stabilizing the unknown system and reducing control cost.
Article
Computer Science, Artificial Intelligence
Mingming Liang, Qinglai Wei
Summary: This paper proposes a partial policy iteration adaptive dynamic programming algorithm to solve the optimal control problem of nonlinear systems. By updating the control law locally, the algorithm reduces computational burden and can be successfully executed on low-performance devices, with convergence analysis and theoretical development provided.
Article
Automation & Control Systems
Cong Li, Yongchao Wang, Fangzhou Liu, Qingchen Liu, Martin Buss
Summary: This article presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems using an incremental adaptive dynamic programming (IADP) approach. It leverages time delay estimation (TDE) technique and measured input-state data to achieve incremental stabilization under uncertainties, disturbances, and saturation. Numerical simulations validate its effectiveness and superiority in reducing energy expenditure and enhancing robustness.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Chun Li, Jinliang Ding, Frank L. Lewis, Tianyou Chai
Summary: This paper introduces a novel formulation of the value function to address the optimal tracking problem of nonlinear discrete-time systems, successfully applied in adaptive dynamic programming algorithms. The optimal control policy can be deduced through this value function, demonstrating the optimality of the obtained control policy.
Article
Automation & Control Systems
Yongwei Zhang, Bo Zhao, Derong Liu, Shunchao Zhang
Summary: This article investigates the event-triggered optimal tracking control problem for multiplayer unknown nonlinear systems using adaptive critic designs. It proposes a novel weight updating rule and derives the optimal tracking control policy for each player by solving coupled event-triggered Hamilton-Jacobi equations. Additionally, an event-triggering condition is designed to ensure the uniform ultimate boundedness of the closed-loop multiplayer systems.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Mingduo Lin, Bo Zhao, Derong Liu
Summary: A model-free optimal tracking controller for discrete-time nonlinear systems is designed using policy gradient adaptive critic designs and experience replay, aiming to improve control performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fuyu Zhao, Weinan Gao, Zhong-Ping Jiang, Tengfei Liu
Summary: This article introduces an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. It reconstructs unmeasurable states and reduces controller updates through an event-based feedback strategy. The iterative solution to the discrete-time algebraic Riccati equation is carried out using event-triggered adaptive dynamic programming, with convergence and closed-loop stability verified using Lyapunov techniques.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Qinglai Wei, Tianmin Zhou, Jingwei Lu, Yu Liu, Shuai Su, Jun Xiao
Summary: In this article, a new stochastic adaptive dynamic programming (ADP) method is developed to solve the optimal control problem of continuous-time (CT) time-invariant nonlinear systems with stochastic nonlinear disturbances. The method simultaneously approximates the value function and the control law under the conditional expectation. The asymptotic stability of the closed-loop stochastic system in probability is analyzed using the stochastic Lyapunov direct method, and the convergence of the developed ADP method is proven. Four simulations are conducted to demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Huaiyuan Jiang, Bin Zhou, Guang-Ren Duan
Summary: In this article, the 1-policy iteration (1-PI) method for the optimal control problem of discrete-time linear systems is reconsidered and restated from a novel aspect. A modified 1-PI algorithm is introduced based on new properties of the traditional 1-PI, with its convergence proven. The data-driven implementation is constructed with a new matrix rank condition, and a simulation example verifies the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zijie Guo, Hongyi Li, Hui Ma, Wei Meng
Summary: This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) using the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. The article introduces a one-to-one mapping technique to transform the constrained systems into equivalent unconstrained systems. It also develops an improved nonquadratic cost function and a novel tuning rule of critic neural network (NN) weights to overcome the issue of difficult persistence of excitation (PE) condition. The stability of the closed-loop system and the convergence of critic NN weights are proved using the Lyapunov stability theorem. Simulation results show the effectiveness of the proposed control strategy for multiple QUAVs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Shunchao Zhang, Bo Zhao, Derong Liu, Cesare Alippi, Yongwei Zhang
Summary: In this article, an event-triggered robust control (ETRC) method is investigated for multi-player nonzero-sum games of continuous-time input constrained nonlinear systems with mismatched uncertainties. The method transforms the robust control problem into an optimal regulation problem by constructing an auxiliary system and designing an appropriate value function. A critic neural network (NN) is used to approximate the value function of each player and obtain control laws. The method reduces computational burden and communication bandwidth by updating the control laws when events occur. The effectiveness of the developed ETRC method is demonstrated through theoretical analysis and examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Qiuye Wu, Bo Zhao, Derong Liu, Marios M. Polycarpou
Summary: This paper proposes an event-triggered adaptive dynamic programming method to solve the decentralized tracking control problem for input constrained unknown nonlinear interconnected systems. A neural-network-based local observer is established to reconstruct the system dynamics using local input-output data and desired trajectories. The DTC problem is transformed into an optimal control problem using a nonquadratic value function. The DTC policy is obtained by solving the local Hamilton-Jacobi-Bellman equation through the observer-critic architecture, with weights tuned by the experience replay technique. Simulation examples demonstrate the effectiveness of the proposed scheme.
Article
Computer Science, Artificial Intelligence
Mingduo Lin, Bo Zhao, Derong Liu
Summary: A novel policy gradient (PG) adaptive dynamic programming method is proposed for nonlinear discrete-time zero-sum games with unknown dynamics. A policy iteration algorithm is used to approximate the Q-function and the control and disturbance policies using neural network approximators. The control and disturbance policies are then updated using the PG method based on the iterative Q-function. The experience replay technique is applied to improve training stability and data usage efficiency. Simulation results show the effectiveness of the proposed method.
Article
Computer Science, Artificial Intelligence
Mingming Liang, Derong Liu
Summary: This article presents a novel neural-network-based optimal event-triggered impulsive control method. The proposed method utilizes a general-event-based impulsive transition matrix (GITM) to represent the evolving characteristics of all system states across impulsive actions. Through the developed event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP), the optimization problems for stochastic systems with event-triggered impulsive controls are addressed. The results show that the proposed methods can reduce computational and communication burdens and fulfill the desired goals.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Mingming Ha, Ding Wang, Derong Liu
Summary: Inspired by the successive relaxation method, a novel discounted iterative adaptive dynamic programming framework is developed, which possesses an adjustable convergence rate for the iterative value function sequence. The convergence properties of the value function sequence and the stability of the closed-loop systems under the new discounted value iteration (VI) are investigated. An accelerated learning algorithm with convergence guarantee is presented based on the properties of the given VI scheme. Amidst the implementation of the new VI scheme and its accelerated learning design, value function approximation and policy improvement are involved. The performance of the developed approaches is verified using a nonlinear fourth-order ball-and-beam balancing plant, showing significant acceleration of the convergence rate of the value function and reduction in computational cost compared to traditional VI.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Danyu Lin, Shan Xue, Derong Liu, Mingming Liang, Yonghua Wang
Summary: In this paper, a problem of multiplayer hierarchical decision-making for non-affine systems is solved using adaptive dynamic programming. The control dynamics are obtained and combined with the original system dynamics, transforming the non-affine multiplayer system into a general affine form. The hierarchical decision problem is modeled as a Stackelberg game, and a neural network is used to reconstruct the augmented system and approximate the value function. The feasibility and effectiveness of the algorithm are confirmed through simulation.
Article
Computer Science, Artificial Intelligence
Chujian Zeng, Bo Zhao, Derong Liu
Summary: This paper proposes a neuro-dynamic programming-based fault tolerant control scheme for a class of nonlinear systems, considering the occurrence of both actuator and sensor faults simultaneously. The scheme combines a descriptor observer with an adaptive observer to estimate system states and multiple faults. By employing a critic neural network, the approximate optimal control policy is obtained for the fault-free system. An FTC law is developed to suppress the influence of actuator faults by combining the estimations of actuator faults with the approximate optimal control policy. The stability of the closed-loop nonlinear system is analyzed using the Lyapunov stability theorem.
Article
Automation & Control Systems
Yongwei Zhang, Bo Zhao, Derong Liu, Shunchao Zhang
Summary: In this article, the event-triggered robust control problem of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated using adaptive dynamic programming. A neural network-based identifier is constructed to relax the requirement of system dynamics. By designing a nonquadratic value function, the stabilization problem is converted into a constrained optimal control problem. The approximate solution of the event-triggered Hamilton-Jacobi equation is obtained using a critic network with a novel weight updating law, and the Lyapunov stability theorem ensures that the multiplayer system is uniformly ultimately bounded.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Mingming Liang, Yonghua Wang, Derong Liu
Summary: In this study, a novel general impulsive transition matrix is defined to reveal the transition dynamics and probability distribution evolution patterns between impulsive events. Based on this matrix, policy iteration-based impulsive adaptive dynamic programming algorithms are developed to solve optimal impulsive control problems. The algorithms demonstrate convergence to the optimal impulsive performance index function and allow for optimization on computing devices with low memory spaces.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Proceedings Paper
Automation & Control Systems
Jinquan Lin, Bo Zhao, Derong Liu
Summary: In this paper, an integral reinforcement learning (IRL)-based approximate optimal control (AOC) method is developed for unknown nonaffine systems using dynamic feedback. The optimal control policy for nonaffine systems cannot be explicitly expressed due to the unknown input gain matrix. Thus, a dynamic feedback signal is introduced to transform the nonaffine system into an augmented affine system. The AOC for unknown nonaffine systems is formulated by designing an appropriate value function for the augmented affine system, and the IRL method is adopted to derive the approximate solution of the Hamilton-Jacobi-Bellman equation.
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS
(2023)
Article
Automation & Control Systems
Bo Zhao, Guang Shi, Derong Liu
Summary: This article investigates local control problems for nonlinear interconnected systems by using adaptive dynamic programming (ADP) with particle swarm optimization (PSO). It constructs a proper local value function and employs a local critic neural network to solve the local Hamilton-Jacobi-Bellman equation. The event-triggering mechanism is introduced to determine the sampling time instants and ensure asymptotic stability through Lyapunov stability analysis.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hui Wang, Zhigang Liu, Zhiwei Han, Yanbo Wu, Derong Liu
Summary: Active pantograph control is a promising technique for improving train's current collection quality. Existing solutions have limitations in handling various operating conditions and lack of adaptability. In this study, a context-based deep meta-reinforcement learning algorithm is proposed to alleviate these problems. Experimental results show that the proposed algorithm can quickly adapt to new conditions and reduce contact force fluctuations.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhanyu Yang, Bo Zhao, Derong Liu
Summary: In this article, a novel pinning control method that requires only partial node information is developed to synchronize drive-response memristor-based neural networks with time delay. An improved mathematical model of the networks is established to accurately describe their dynamic behaviors. Unlike previous literature that requires information from all nodes, the proposed method only relies on local information to achieve synchronization of delayed networks, reducing communication and calculation burdens. Sufficient conditions for synchronization are provided, and numerical simulation and comparative experiments validate the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Mingduo Lin, Bo Zhao, Derong Liu
Summary: In this article, an event-triggered robust adaptive dynamic programming (ETRADP) algorithm is proposed to solve multiplayer Stackelberg-Nash games (MSNGs) for uncertain nonlinear continuous-time systems. The hierarchical decision-making process considering different roles of players is described, transforming the robust control problem into an optimal regulation problem. An online policy iteration algorithm is used to solve the derived Hamilton-Jacobi equation with an event-triggered mechanism to reduce computational and communication burdens. Critic neural networks (NNs) are constructed to obtain the event-triggered approximate optimal control policies for all players, ensuring the stability of the closed-loop system.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Mingming Liang, Derong Liu
Summary: This article focuses on designing the optimal impulsive controller (IMC) of continuous-time nonlinear systems and proposes a new adaptive dynamic programming algorithm with high generality and feasibility. The introduced policy-improving mechanism makes the algorithm more flexible for memory-limited computing devices.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
