Multiple-model predictive control framework for multi-input multi-output continuous processes

Transition control over a large operating space presents a challenging problem, especially for nonlinear multiple-input multiple-output (MIMO) constrained systems. Attractive features of a transition control structure should include accurate, rapid, and stable closed-loop response. A large number of recent approaches address this issue using multiple fixed and adaptive models, and single or multiple controllers. Regardless, the controller must not only successfully regulate the plant at the operating points, but also track the reference trajectory during the transition. In fact, the problem can be considered in two parts—the identification of a model that estimates the plant outputs and the synthesis of a suitable controller that produces smooth and practical control action. Satisfactory closed-loop and stable performance of the controller and nonlinear plant is inferred, if the performance of the model in closed-loop with the controller can be guaranteed.; This work proposes and develops an approach to transition control based on the development of a state-shared model. Here, a state-shared model is defined as a linear time-invariant state-space structure that is a realization of the nonlinear system and driven by the measured signals—the plant outputs and the manipulated variables. The coefficient matrices in the state-shared model are selected to be state controllable by the designer, however, the measurement equation of each transition state is unique. The description of the measurements is embedded in the coefficient matrix. Any such realization necessarily fulfills the requirement that the output of the state-shared model must be an asymptotically correct estimate of the output of the plant, if the process models were selected appropriately. The parameters of the adaptive models are modified using a stable and convergent adaptive procedure. By means of adapting the parameters of the measurement equation and guaranteeing stable switching among fixed and adaptive models, accurate estimates of the system can be obtained. Because of the state-shared model, the assumption that the fixed models cover the large operating space is replaced by the number of parameters to be adapted. To address this issue, model order reduction methods can be used to obtain a low order state-shared model, thereby reducing the number of parameters to be adapted. The theoretical underpinnings that permit the development of the state-shared model are stated and proven. Using this state-shared model, model based controller methods can be used to synthesize appropriate controllers. In this work, an optimal controller based on the theory of model predictive control (MPC) and a robust controller based on the H-infinity control theory are studied with the state-shared model. Conditions for both types of controllers to produce stable closed-loop responses for certain classes of systems is used to established closed-loop stability in the case of transition control.; Transition control using the state-shared model in either an H_∞ or MPC framework is demonstrated on several single-input single-output systems, a multiple-input multiple-output two-phase reactor system, and a nonlinear plant described as the Tennessee Eastman Challenge problem.