Improving the performance of globalized dual heuristic programming for fault tolerant control through an online learning supervisor

An advanced reconfigurable controller enhanced by a multiple model architecture is proposed as a tool to achieve fault tolerance in complex nonlinear systems. The most complete adaptive critic design, globalized dual heuristic programming (GDHP), constitutes a highly flexible nonlinear adaptive controller responsible for the generation of new control solutions for novel plant dynamics introduced by unknown faults. The main contribution of the presented work focuses on a novel fault tolerant control supervisor. Working on a higher hierarchical level, the proposed supervisor makes use of two quality indices to perform fault detection, identification and isolation based on the knowledge stored in a dynamic model bank (DMB). In the event of abrupt known faults, such knowledge is then used to greatly reduce the reconfiguration time of the GDHP controller. The synergy of the proposed supervisor and GDHP goes beyond, as solutions designed by the controller to previously unknown faults are autonomously added to the model bank. The fine interrelations between the algorithm's subsystems and its advanced capabilities are illustrated through extensive numerical simulations of a single-input single-output (SISO) linear system and of a multiple-input multiple-output (MIMO) nonlinear system, both subject to a series of fault scenarios involving expected and unexpected, abrupt and incipient faults.