Article
Computer Science, Artificial Intelligence
Lijie Wang, Jiahong Xu, Yang Liu, C. L. Philip Chen
Summary: This article investigates the optimal consensus control problem for multiagent systems with input constraints. It proposes a single critic neural network with time-varying activation function for approximate optimal control and an improved learning law for weight update. It also designs an effective dynamic event-triggering mechanism to improve the utilization rate of communication resource. A simulation example is provided to support the effectiveness of the proposed method and the superiority of the designed mechanism.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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
Automation & Control Systems
Shangwei Zhao, Jingcheng Wang, Haotian Xu, Hongyuan Wang
Summary: In this paper, an approximate dynamic programming approach is proposed for handling the robust optimal tracking control problem in switched systems with uncertainties. A neural network based identifier is used to estimate the unknown system dynamics, and actor-critic neural networks are constructed to approximate the optimal control input and performance index. The convergence of the proposed approach is proved, and numerical simulations are conducted to validate its effectiveness.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Xiong Yang, Mengmeng Xu, Qinglai Wei
Summary: We study the dynamic event-driven Hop constrained control problem through approximate dynamic programming (ADP). Differing from the existing literature considering systems with either symmetric constraints or asymmetric constraints, we consider the two different constraints simultaneously. Initially, by constructing a generalized nonquadratic value function, we transform the H-8 constrained control problem into an unconstrained two-player zero-sum game. Then, we present an event-driven Hamilton-Jacobi-Isaacs equation (ED-HJIE) corresponding to the zero-sum game for lowering down the computational load. To solve the ED-HJIE, we propose a dynamic triggering mechanism together with a sole critic neural network (CNN) being built under the ADP framework. The CNN's weights are tuned via the gradient descent approach. After that, we prove uniform ultimate boundedness of the closed-loop system and the CNN's weight estimation error via Lyapunov's method. Finally, we separately use an F16 aircraft plant and an inverted pendulum system to validate the present theoretical claims.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Xiao Han, Lei Liu, Huijin Fan, Zhongtao Cheng
Summary: This paper investigates the robust approximate optimal control problem for air-breathing hypersonic vehicle (AHV) and proposed a robust approximate optimal controller based on adaptive dynamic programming (ADP). By input-output linearization and introducing auxiliary variables, the high-order nonlinear AHV dynamics are transformed to a second-order feedback decoupling model. The proposed controller considers both robustness and input cost, optimizes control actions, and avoids chattering. The use of a single critic network design ensures system stability and simplifies implementation. Simulation results demonstrate the effectiveness of the proposed scheme.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Tohid Sardarmehni, Xingyong Song
Summary: This article studies optimal tracking in switched systems with fixed mode sequence and free final time. The switching times and final time are treated as parameters in the optimal control problem formulation. Approximate dynamic programming (ADP) is used to solve the problem, with an inner loop to converge to the optimal policy at each time step. A new method is introduced to decrease the computational burden by using evolving suboptimal policies to learn the optimal solution. The effectiveness of the proposed solutions is evaluated through numerical simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Ding Wang, Peng Xin, Mingming Zhao, Junfei Qiao
Summary: The constrained receding-horizon heuristic dynamic programming (RH-HDP) algorithm is established in this article to address the approximate optimal control problem of nonlinear affine systems with the terminal state constraint and asymmetric control constraints. The approximate optimal control problem is transformed into a battery of subproblems based on the RH mechanism of model predictive control (MPC). The algorithm considers the terminal state constraint and introduces asymmetric control constraints to confine the control input within the given constraint range.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Chaofan Zhang, Guoshan Zhang, Qi Dong
Summary: This paper proposed a nearly optimal control scheme based on fixed-time disturbance observer for reusable launch vehicle, which has shown effectiveness in dealing with model uncertainties, input constraints, and unknown disturbances.
Article
Automation & Control Systems
Mario Sassano, Alessandro Astolfi
Summary: This paper presents a fixed-point characterization and a constructive condition for the optimal costate in finite-horizon optimal control problems for nonlinear systems. The abstract property is translated into a system of algebraic equations, achieving the desired degree of accuracy while consistently consisting of a number of equations equal to the dimension of the state space. Additionally, a dual characterization of the optimal terminal value of the state is discussed, along with computational aspects of the proposed strategy.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Shuhua Gao, Changkai Sun, Cheng Xiang, Kairong Qin, Tong Heng Lee
Summary: This article investigates the finite-horizon optimal control problem of Boolean control networks from a graph theory perspective. Two general problems are formulated to unify various special cases, and algorithms are developed to solve them with reduced computational complexity.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Mathieu Granzotto, Romain Postoyan, Lucian Busoniu, Dragan Nesic, Jamal Daafouz
Summary: This paper analyzes the stability of deterministic nonlinear discrete-time systems, constructs a Lyapunov function for the closed-loop system, and ensures a uniform semiglobal stability property with adjustable parameters including the discount factor and horizon length. It provides less conservative stability conditions, new relationships between optimal value functions, and investigates stability with only a near-optimal sequence of inputs for discounted finite-horizon costs.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Multidisciplinary Sciences
Yanping Gao, Zuojun Liu
Summary: This paper proposes a novel adaptive fixed-time disturbance observer (AFXDO) based approximate optimal tracking control architecture for nonlinear systems with partially unknown dynamic drift and perturbation. The AFXDO is designed to attenuate the impact of disturbance without prior information, ensuring that the observer errors converge to zero in a fixed time. The approximate optimal control is achieved by incorporating the real-time estimation of AFXDO into a critic-only ADP framework, resulting in fast transient performance and low control consumptions.
Article
Automation & Control Systems
Cong Li, Qingchen Liu, Zhehua Zhou, Martin Buss, Fangzhou Liu
Summary: This article proposes an off-policy risk-sensitive reinforcement learning-based control framework to jointly optimize the task performance and constraint satisfaction in a disturbed environment. The risk-aware value function, constructed using the pseudo control and risk-sensitive input and state penalty terms, is introduced to convert the original constrained robust stabilization problem into an equivalent unconstrained optimal control problem. Simulation results reveal the validity of the proposed control framework.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Qinglai Wei, Liao Zhu, Tao Li, Derong Liu
Summary: This article develops a new time-varying adaptive dynamic programming (ADP) algorithm to solve finite-horizon optimal control problems for a class of discrete-time affine nonlinear systems. Inspired by the pseudolinear method, the nonlinear system can be approximated by a series of time-varying linear systems. The paper proves the convergence of the states of the time-varying linear systems to the states of the discrete-time affine nonlinear systems and the convergence of the iterative value functions and control laws to the optimal ones. Numerical results are provided to verify the effectiveness of the proposed method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Aerospace
Zhibing Li, Xiaoyue Zhang, Huanrui Zhang, Feng Zhang
Summary: This study proposes a novel three-dimensional approximate cooperative integrated guidance and control (ACIGC) scheme for multiple hypersonic skid-to-turn (STT) missiles simultaneously attacking ground-maneuver targets. The scheme combines backstepping control, sliding mode control, dynamic surface control, and a reduced-order extended state observer. The proposed ACIGC scheme considers fixed-impact azimuth and time constraints, and demonstrates its effectiveness and robustness through simulation results and Monte Carlo simulations.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
