Addressing to online adaptive controller malfunction in fault tolerant control

A complete fault tolerant control solution calls for a nonlinear adaptive controller with universal approximation capability and guaranteed stability. To fulfill this requirement, we propose the use of neural networks trained online under a globalized dual heuristic programming architecture supervised by a decision logic capable of identifying controller malfunctions in the early stages and providing new avenues with greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions and of the faults in the system are achieved through three independent quality indexes. Proof-of-the-concept simulations of nonlinear plants demonstrate the approach legitimacy.