Industrial process minimum-maximum optimization fault-tolerant control method based on reinforcement learning

The invention relates to the technical field of industrial control, in particular to an industrial process minimum-maximum optimization fault-tolerant control method based on reinforcement learning. Comprising the following steps: (1) establishing an augmented state space model containing tracking errors and state increments on the basis of an original system state space model with actuator faults and external disturbance, and proposing a performance index function according to the augmented state space model; (2) proposing a value function and a Q function according to the performance index function, and constructing corresponding expressions of optimal control input, worst external disturbance, optimal control gain and worst external disturbance gain; (3) giving initial control gain and external disturbance gain capable of stabilizing the system to collect data theta j (k) and rho kj, wherein theta j (k) and rho kj are data which are generated by jth iteration and contain system production information; (4)