Dual fault‐tolerant control for a class of stochastic systems with partial loss‐of‐control effectiveness

Loss‐of‐control effectiveness is a common fault in the engineering field. In the existing adaptive fault‐tolerant control algorithms, observers and Lyapunov functions are often used to estimate fault parameters. However, they can only ensure the convergence of fault parameters as time goes to infinity. This leads to the fact that the controller can only play a better fault‐tolerant role in the steady‐state process, and cannot guarantee the system performance in the transient state. Therefore, a dual fault‐tolerant control (DFTC) algorithm is proposed for a class of stochastic systems with partial loss‐of‐control effectiveness. The algorithm introduces a fault‐tolerant objective on the basis of the control objective. By establishing a new objective function, the controller is forced to have the characteristics of active learning to improve the transient performance of the fault system. An example simulation verifies the validity of DFTC. The simulation results show that DFTC can drive the system to the desired output, especially when partial loss‐of‐control efficiency occurs in the system, it can learn the unknown parameters actively, quickly, and accurately, which is significantly better than the conventional control based on the certainty equivalence principle and improves the reliability of the system.