|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming显示文摘This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems.The main idea is to use the adaptive dynamic programming(ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery.Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally,simulation and comparison results are given to illustrate the performance of the presented method. | Qinglai Wei Derong Liu Yu Liu Ruizhuo Song | 2017 | IEEE/CAA Journal of Automatica Sinica2017，4，2： | 13 |
| 2 | A new self-learning optimal control laws for a class of discrete-time nonlinear systems based on ESN architecture显示文摘A novel self-learning optimal control method for a class of discrete-time nonlinear systems is proposed based on iteration adaptive dynamic programming(ADP)algorithm.It is proven that the iteration costate functions converge to the optimal one,and a detailed convergence analysis of the iteration ADP algorithm is given.Furthermore,echo state network(ESN)architecture is used as the approximator of the costate function for each iteration.To ensure the reliability of the ESN approximator,the ESN mean square training error is constrained in the satisfactory range.Two simulation examples are given to demonstrate that the proposed control method has a fast response speed due to the special structure and the fast training process. | SONG RuiZhuo XIAO WenDong SUN ChangYin | 2014 | Science China(Information Sciences)2014，57，6： | 4 |
| 3 | Optimal Fixed-Point Tracking Control for Discrete-Time Nonlinear Systems via ADP显示文摘Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value,and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks(NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method. | Ruizhuo Song Liao Zhu | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，3： | 3 |
| 4 | Optimal Synchronization Control of Heterogeneous Asymmetric Input-Constrained Unknown Nonlinear MASs via Reinforcement Learning显示文摘The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems(MASs)is considered in the paper.Intuitively,a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system.Then,considering that the leader’s information is not available to every follower,a novel distributed observer is designed to estimate the leader’s state using only exchange of information among neighboring followers.After that,a network of augmented systems is constructed by combining observers and followers dynamics.A nonquadratic cost function is then leveraged for each augmented system(agent)for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman(HJB)equation is solved in a data-based fashion.More specifically,a data-based off-policy reinforcement learning(RL)algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents’dynamics.Convergence of the improved RL algorithm to the solution to the constrained HJB equation is also demonstrated.Finally,the correctness and validity of the theoretical results are demonstrated by a simulation example. | Lina Xia Qing Li Ruizhuo Song Hamidreza Modares | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，3： | 2 |
| 5 | Finite-time leader-follower consensus of a discrete-time system via slidingmode control显示文摘In this study,we solve the finite-time leader-follower consensus problem of discrete-time second-order multi-agent systems(MASs)under the constraints of external disturbances.First,a novel consensus scheme is designed using a novel adaptive sliding mode control theory.Our adaptive controller is designed using the traditional sliding mode reaching law,and its advantages are chatter reduction and invariance to disturbances.In addition,the finite-time stability is demonstrated by presenting a discrete Lyapunov function.Finally,simulation results are presented to prove the validity of our theoretical results. | Ruizhuo SONG Shi XING Zhen XU | 2022 | Frontiers of Information Technology & Electronic Engineering2022，23，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员