A Model-Based Direct Adaptive Control Strategy for Nonlinear Processes

In this paper, a novel model-based direct adaptive control strategy is proposed for nonlinear processes whose mathematical models are input-output linearizable. The proposed controller consists of two parts: the first part includes the nonlinear process information gained directly from the process's nominal model and the second part, which has two adjustable parameters, is an adaptive nonlinear function used to compensate for model errors adaptively. An efficient parameter tuning algorithm is derived for use to adjust the parameters of the adaptive term by simply feeding back the process output information. To show the robust stability and performance of the direct adaptive control system, a rigorous analysis involving the use of a Lyapunov-based approach is presented. The effectiveness and applicability of the proposed nonlinear strategy are demonstrated through the temperature trajectory tracking control problem of a batch reactor in the presence of plant/model mismatch, unanticipated periodic disturbances and process noise. Extensive simulation results reveal that the proposed model-based direct adaptive control strategy is promising and that it provides stable, robust and high control performance for nonlinear processes.