A Novel Resilient Control Scheme for a Class of Markovian Jump Systems With Partially Unknown Information
针对一类转移概率完全未知的非线性马尔可夫跳变系统，提出一种基于强化学习的弹性控制算法，通过将控制与攻击视为博弈对手，利用系统数据求解零和博弈，无需转移概率信息，并在多模式机械臂系统上验证了有效性。
In the complex practical engineering systems, many interferences and attacking signals are inevitable in industrial applications. This article investigates the reinforcement learning (RL)-based resilient control algorithm for a class of Markovion jump systems with completely unknown transition probability information. Based on the Takagi-Sugeno logical structure, the resilient control problem of the nonlinear Markovion systems is converted into solving a set of local dynamic games, where the control policy and attacking signal are considered as two rival players. Combining the potential learning and forecasting abilities, the new integral RL (IRL) algorithm is designed via system data to compute the zero-sum games without using the information of stationary transition probability. Besides, the matrices of system dynamics can also be partially unknown, and the new architecture requires less transmission and computation during the learning process. The stochastic stability of the system dynamics under the developed overall resilient control is guaranteed based on the Lyapunov theory. Finally, the designed IRL-based resilient control is applied to a typical multimode robot arm system, and implementing results demonstrate the practicality and effectiveness.