Wang Ding, Qiao Junfei, Cheng Long
IEEE Trans Cybern. 2022 Jan;52(1):77-86. doi: 10.1109/TCYB.2020.2977318. Epub 2022 Jan 11.
The adaptive optimal feedback stabilization is investigated in this article for discounted guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis, the guaranteed cost control problem involving a discounted utility is transformed to the design of a discounted optimal control policy for the nominal plant. The size of the neighborhood with respect to uniformly ultimately bounded stability is discussed. Then, for deriving the approximate optimal solution of the modified Hamilton-Jacobi-Bellman equation, an improved self-learning algorithm under the framework of adaptive critic designs is established. It facilitates the neuro-optimal control implementation without an additional requirement of the initial admissible condition. The simulation verification toward several dynamics is provided, involving the F16 aircraft plant, in order to illustrate the effectiveness of the discounted guaranteed cost control method.
本文研究了不确定非线性动力系统的折扣保成本控制的自适应最优反馈镇定问题。通过理论分析，将涉及折扣效用的保成本控制问题转化为标称对象的折扣最优控制策略设计。讨论了关于一致最终有界稳定性的邻域大小。然后，为了推导修正的哈密顿-雅可比-贝尔曼方程的近似最优解，在自适应评判设计框架下建立了一种改进的自学习算法。它便于神经最优控制的实现，而无需额外的初始容许条件要求。针对包括F16飞机对象在内的几种动力学进行了仿真验证，以说明折扣保成本控制方法的有效性。
