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| 1 | Continuous Sliding Mode Controller with Disturbance Observer for Hypersonic Vehicles显示文摘In this paper, a continuous sliding mode controller with disturbance observer is proposed for the tracking control of hypersonic vehicles to suppress the chattering. The finite time disturbance observer is involved to make that the continuous sliding mode controller has the property of disturbance rejection.Due to continuous terms replacing the discontinuous term of traditional sliding mode control, switching modes of velocity and altitude firstly arrive at small regions with respect to disturbance observation errors. Switching modes keep zero and velocity and altitude asymptotically converge to their reference commands after disturbance observation errors disappear. Simulation results have proved the proposed method can guarantee the tracking of velocity and altitude with continuous sliding mode control laws,and also has the fast convergence rate and robustness. | Chaoxu Mu Qun Zong Bailing Tian Wei Xu | 2015 | IEEE/CAA Journal of Automatica Sinica2015，2，1： | 11 |
| 2 | A weighted LS-SVM approach for the identification of a class of nonlinear inverse systems显示文摘In this paper,a weighted least square support vector machine algorithm for identification is proposed based on the T-S model. The method adopts fuzzy c-means clustering to identify the structure. Based on clustering,the original input/output space is divided into several subspaces and submodels are identified by least square support vector machine(LS-SVM) . Then,a regression model is constructed by combining these submodels with a weighted mechanism. Furthermore we adopt the method to identify a class of inverse systems with immeasurable state variables. In the process of identification,an allied inverse system is constructed to obtain enough information for modeling. Simulation experiments show that the proposed method can identify the nonlinear allied inverse system effectively and provides satisfactory accuracy and good generalization. | SUN ChangYin MU ChaoXu LI XunMing | 2009 | Science in China(Series F)2009，52，5： | 8 |
| 3 | Characteristic Model-based Discrete-time Sliding Mode Control for Spacecraft with Variable Tilt of Flexible Structures显示文摘In this paper, the finite-time attitude tracking control problem for the spacecrafts with variable tilt of flexible appendages in the conditions of exogenous disturbances and inertia uncertainties is addressed. First the characteristic modeling method is applied to the problem of the spacecraft modeling.Second, a novel adaptive sliding mode surface is designed based on the characteristic model. Furthermore, a discrete-time sliding mode control(DTSMC) law, which makes the tracking error converge into a predefined bound in finite time, is proposed by employing the parameters of characteristic model associated with the sliding mode surface to provide better performances,robustness, faster response, and higher control precision. The designed DTSMC includes the adaptive control architecture and is chattering-free. Finally, digital simulations of a sun synchronous orbit satellite(SSOS) are presented to illustrate effectiveness of the control strategies as well as to verify the practical feasibility of the rapid maneuver mission. | Lei Chen Yan Yan Chaoxu Mu Changyin Sun | 2016 | IEEE/CAA Journal of Automatica Sinica2016，3，1： | 5 |
| 4 | Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism显示文摘Optimal feedback design of dynamical systems is a significant topic in automatic control community and information science.As for nonlinear systems,optimal control design always leads to coping with the nonlinear Hamilton-Jacobi-Bellman equation.Nevertheless,it is intractable to acquire the analytic solution of the nonlinear Hamilton-JacobiBellman equation for general nonlinear systems. | Ding WANG Chaoxu MU | 2017 | Science China(Information Sciences)2017，60，5： | 4 |
| 5 | Learning-based control for discrete-time constrained nonzero-sum games显示文摘A generalized policy-iteration-based solution to a class of discrete-time multi-player nonzero-sum games concerning the control constraints was proposed.Based on initial admissible control policies,the iterative value function of each player converges to the optimum approximately,which is structured by the iterative control policies satisfying the Nash equilibrium.Afterwards,the stability analysis is shown to illustrate that the iterative control policies can stabilize the system and minimize the performance index function of each player.Meanwhile,neural networks are implemented to approximate the iterative control policies and value functions with the impact of control constraints.Finally,two numerical simulations of the discrete-time two-player non-zero-sum games for linear and non-linear systems are shown to illustrate the effectiveness of the proposed scheme. | Chaoxu Mu Jiangwen Peng Yufei Tang | 2021 | CAAI Transactions on Intelligence Technology2021，6，2： | 1 |
| 6 | Data-based intelligent modeling and control for nonlinear systems显示文摘With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested. | Chaoxu MU Changyin SUN | 2011 | Frontiers of Electrical and Electronic Engineering in China2011，6，2： | 0 |
| 7 | Heuristic dynamic programming-based learning control for discrete-time disturbed multi-agent systems显示文摘Owing to extensive applications in many fields,the synchronization problem has been widely investigated in multi-agent systems.The synchronization for multi-agent systems is a pivotal issue,which means that under the designed control policy,the output of systems or the state of each agent can be consistent with the leader.The purpose of this paper is to investigate a heuristic dynamic programming(HDP)-based learning tracking control for discrete-time multi-agent systems to achieve synchronization while considering disturbances in systems.Besides,due to the difficulty of solving the coupled Hamilton–Jacobi–Bellman equation analytically,an improved HDP learning control algorithm is proposed to realize the synchronization between the leader and all following agents,which is executed by an action-critic neural network.The action and critic neural network are utilized to learn the optimal control policy and cost function,respectively,by means of introducing an auxiliary action network.Finally,two numerical examples and a practical application of mobile robots are presented to demonstrate the control performance of the HDP-based learning control algorithm. | Yao Zhang Chaoxu Mu Yong Zhang Yanghe Feng | 2021 | Control Theory and Technology2021，19，3： | 0 |
| 8 | Neural network-based adaptive decentralized learning control for interconnected systems with input constraints显示文摘In this paper,the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints.Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem,the decentralized control strategy can be established by a series of optimal control policies.A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem.This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function,respectively.The additional stabilizing term is introduced and an improved weight updating law is derived,which relaxes the requirement of initial admissible control policy.Besides,the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints.The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory.Finally,the effectiveness of the proposed decentralized learning control method is verified by simulation results. | Chaoxu Mu Hao Luo Ke Wang Changyin Sun | 2021 | Control Theory and Technology2021，19，3： | 0 |
| 9 | A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems显示文摘In this paper,a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems.Motivated by the classical on-policy and off-policy algorithms of reinforcement learning,the online feedback relearning(FR)algorithm is developed where the collected data includes the influence of disturbance signals.The FR algorithm has better adaptability to environmental changes(such as the control channel disturbances)compared with the off-policy algorithm,and has higher computational efficiency and better convergence performance compared with the on-policy algorithm.Data processing based on experience replay technology is used for great data efficiency and convergence stability.Simulation experiments are presented to illustrate convergence stability,optimality and algorithmic performance of FR algorithm by comparison. | Chaoxu Mu Yong Zhang Guangbin Cai Ruijun Liu Changyin Sun | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，5： | 0 |
| 10 | Analytical reentry guidance framework based on swarm intelligence optimization and altitude-energy profile显示文摘Aimed at improving the real-time performance of guidance instruction generation,an analytical hypersonic reentry guidance framework is presented.The key steps of the novel guidance framework are the parameterization of reentry guidance problems and the optimization of parameters.First,a quintic polynomial function of energy was designed to describe the altitude profile.Then,according to the altitude-energy profile,the altitude,velocity,flight path angle,and bank angle were obtained analytically,which naturally met the terminal constraints.In addition,the angle of the attack profile was determined using the velocity parameter.The swarm intelligent optimization algorithms were used to optimize the parameters.The path constraints were enforced by the penalty function method.Finally,extensive simulations were carried out in both nominal and dispersed cases,and the simulation results showed that the proposed guidance framework was effective,high-precision,and robust in different scenarios. | Hui XU Guangbin CAI Chaoxu MU Xin LI | 2023 | Chinese Journal of Aeronautics2023，36，12： | 0 |
| 11 | Adaptive composite frequency control of power systems using reinforcement learning显示文摘With the incorporation of renewable energy,load frequency control(LFC)becomes more challenging due to uncertain power generation and changeable load demands.The electric vehicle(EV)has been a popular transportation and can also provide flexible options to play a role in frequency regulation.In this paper,a novel adaptive composite controller is designed to solve the LFC problem for the interconnected power system with electric vehicles and wind turbine.EVs are used as regulation resources to effectively compensate the power mismatch.First,the sliding mode controller is developed to reduce the random influences caused by the wind turbine generation system.Second,an auxiliary controller with reinforcement learning is proposed to produce adaptive control signals,which will be attached to the primary proportion-integration-differentiation control signal in a realtime manner.Finally,by considering random wind power,load disturbances and output constraints,the proposed scheme is verified on a two-area power system under four different cases.Simulation results demonstrate that the proposed adaptive composite frequency control scheme has a competitive performance with regard to dynamic performance. | Chaoxu Mu Ke Wang Shiqian Ma Zhiqiang Chong Zhen Ni | 2022 | CAAI Transactions on Intelligence Technology2022，7，4： | 0 |
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