共被期刊论文引用了4次
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| 1 | 执行器故障不确定非线性系统最优自适应输出跟踪控制显示文摘本文针对一类具有执行器故障的多输入多输出(Multi-input multi-output, MIMO)不确定连续仿射非线性系统,提出了一种最优自适应输出跟踪控制方案.设计了保证系统稳定性的不确定项估计神经网络权值调整算法,仅采用评价网络即可同时获得无限时域代价函数和满足哈密顿–雅可比–贝尔曼(Hamilton-Jacobi-Bellman, HJB)方程的最优控制输入.考虑执行器卡死和部分失效故障,设计最优自适应补偿控制律,所设计的控制律可以实现对参考输出的一致最终有界跟踪.飞行器控制仿真和对比验证表明了本文方法的有效性和优越性. | 张绍杰 吴雪 刘春生 | 2018 | 自动化学报2018，44，12： | 9 |
| 2 | Adaptive Linear Quadratic Regulator for Continuous-Time Systems With Uncertain Dynamics显示文摘In this paper, adaptive linear quadratic regulator(LQR) is proposed for continuous-time systems with uncertain dynamics. The dynamic state-feedback controller uses inputoutput data along the system trajectory to continuously adapt and converge to the optimal controller. The result differs from previous results in that the adaptive optimal controller is designed without the knowledge of the system dynamics and an initial stabilizing policy. Further, the controller is updated continuously using input-output data, as opposed to the commonly used switched/intermittent updates which can potentially lead to stability issues. An online state derivative estimator facilitates the design of a model-free controller. Gradient-based update laws are developed for online estimation of the optimal gain. Uniform exponential stability of the closed-loop system is established using the Lyapunov-based analysis, and a simulation example is provided to validate the theoretical contribution. | Sumit Kumar Jha Shubhendu Bhasin | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，3： | 3 |
| 3 | Adaptive dynamic programming for linear impulse systems显示文摘We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming(ADP)method.For linear impulse systems,the optimal objective function is shown to be a quadric form of the pre-impulse states.The ADP method provides solutions that iteratively converge to the optimal objective function.If an initial guess of the pre-impulse objective function is selected as a quadratic form of the pre-impulse states,the objective function iteratively converges to the optimal one through ADP.Though direct use of the quadratic objective function of the states within the ADP method is theoretically possible,the numerical singularity problem may occur due to the matrix inversion therein when the system dimensionality increases.A neural network based ADP method can circumvent this problem.A neural network with polynomial activation functions is selected to approximate the pre-impulse objective function and trained iteratively using the ADP method to achieve optimal control.After a successful training,optimal impulse control can be derived.Simulations are presented for illustrative purposes. | Xiao-hua WANG Juan-juan YU Yao HUANG Hua WANG Zhong-hua MIAO | 2014 | Journal of Zhejiang University-Science C(Computers and Electronics)2014，15，1： | 0 |
| 4 | A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations显示文摘In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and algebraic parts.In the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy. | Behzad Farzanegan Mohsen Zamani Amir Abolfazl Suratgar Mohammad Bagher Menhaj | 2021 | Control Theory and Technology2021，19，2： | 0 |
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