Accommodating controller malfunctions through fault tolerant control architecture

In previous work we have proposed a supervised globalized dual heuristic programming (GDHP) controller as a solution to the fault tolerant control (FTC) problem of nonlinear plants subject to abrupt and incipient faults capable of drastically modifying the system dynamics to maintain stability and performance. The neural network (NN) based adaptive critic controller presented the best choice for the flexibility and power necessary to accomplish the task, however no success guarantees can be made for the online training of neural weights for the unrestricted fault recovery problem. Built on the existing framework, we propose a novel supervisory system capable of detecting controller malfunctions before the stability of the plant is compromised. Furthermore, due to its ability to discern between controller malfunctions and faults within the plant, the proposed supervisor acts in a specific fashion in the event of a controller malfunction to provide new avenues with a greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions from the faults in the plant itself is achieved through an advanced decision logic based on three independent quality indexes. Proof-of-the-concept simulations over a nonlinear plant demonstrate the validity of the approach.