Improving the Performance of Globalized Dual Heuristic Programming for Fault Tolerant Control Through an Online Learning Supervisor

Abstract

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. Note to Practitioners-The raising complexity of physical plants and control missions inevitably leads to increasing occurrence, diversity and severity of faults. Take automated production process as an example, the extent of time a plant is capable of maintaining acceptable performance levels is now considered to be the single factor with the highest impact on profitability. For a growing number of plants, it has become impractical to list all possible fault scenarios in order to take the necessary steps to assure continuous healthy operation. Therefore, it is now essential to have a control algorithm dedicated to the provision of tailored control solutions capable of maintaining stability and as much performance as possible during the occurrence of faults. This is the goal of fault tolerant control. In the presented paper, the most complete adaptive critic design, globalized dual heuristic programming (GDHP), is responsible for the generation of new control solutions for novel plant dynamics introduced by faults unknown at design time. A highly flexible nonlinear adaptive controller, GDHP is capable of dealing with both abrupt and incipient (gradually changing dynamics) faults. Working on a higher hierarchical level, a novel fault supervisor is introduced which 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 convergence time of the GDHP controller. The synergy of the proposed supervisor and GDHP goes further, as solutions designed by the controller to previously unknown faults are autonomously added to the model bank in fact allowing the supervisor to learn new fault scenarios and their solutions as they occur. The fine interrelations between the algorithm's subsystems and its advanced capabilities are illustrated through extensive numerical simulations.