Model-Free Fuzzy Adaptive Control for MIMO Systems

Control of multi-input multi-output (MIMO) plants poses a challenging control problem. The problem becomes much more complicated if the degree of interaction between the inputs and outputs is high. Most of the MIMO control schemes do not take into consideration the cross-coupling effect and are based on either decoupled or decentralized control schemes. These schemes can offer acceptable control performance if the degree of interaction is low. Ignoring the interaction is unrealistic for many plants and employing multiple MISO controllers results in degraded control performance. Almost all such strategies require a plant model a priori. In this work, an extension of SISO model-free fuzzy adaptive controller (MFFAC) is proposed for MIMO plants while taking into consideration the cross-coupling effect. No a priori knowledge about the plant is assumed, and the control scheme does not make use of any decoupler. SISO MFFAC is based on a special type of reinforcement learning, i.e., feedback error learning (FEL). The control architecture of SISO MFFAC is modified to work with MIMO plants by proposing a novel FEL scheme. The novel FEL mechanism incorporates the interaction between the input and output of a MIMO process by using relative gain array (RGA). Simulation results have demonstrated the efficacy of the proposed MIMO control scheme.