NONLINEAR MODEL PREDICTIVE CONTROL DESIGN AND APPLICATIONS

Abstract

<p>This thesis considers the problem of nonlinear predictive control design and applications. A predictive control formulation is presented which expands on the set of initial conditions for which closed-loop stability can be achieved. The key idea in this control design is to utilize control-law independent characterization of the process dynamics subject to constraints via model predicative controllers. An application of this idea is presented to the case of linear process systems for which characterizations of the null controllable region (the set of initial conditions from where closed-loop stability can be achieved subject to input constraints) are available. A predictive controller is designed that achieves closed-loop stability for every initial condition in the null controllable region. For nonlinear process systems, the constraints within the predictive controller are formulated to require the process to evolve within the region from where continued decay of the Lyapunov function value is achievable and incorporated in the predictive control design, thereby expanding on the set of initial conditions from where closed-loop stability can be achieved. The proposed method is illustrated using a chemical reactor example, and the robustness with respect to parametric uncertainty and disturbances demonstrated via application to a styrene polymerization process.</p> <p>In addition, we also consider the application of the predictive control design to the problem of handling actuator faults in nonlinear continuous-time processes and transport-reaction systems. Specifically, we consider faults that preclude the possibility of continued operating at the nominal equilibrium point using the existing robust or reconfiguration-based fault-tolerant control approaches. The key consideration is to operate the plant using the depleted control action at an appropriate safe-park point to prevent onset of hazardous situations as well as enable smooth resumption of nominal operation upon fault-repair. For the case of continuous-time nonlinear process systems we consider the presence of input constraints, uncertainty, and availability of limited measurements. First a Lyapunov-based predictive controller with an explicitly characterized stability region is developed to handle the aforementioned conditions. This control design is then subsequently used to develop a safe-parking framework in the presence of uncertainty, and availability of limited measurements. The proposed framework is illustrated using a chemical reactor example and demonstrated on a styrene polymerization process. Finally, we consider the problem of model predictive control and handling actuator faults in transport-reaction processes described by quasi-linear parabolic partial differential equations (PDEs) subject to input constraints. A Lyapunov-based model predictive controller is designed which accounts for the distributed nature of transport-reaction processes and provides an explicit characterization of the set of initial conditions from where closed-loop stability of the parabolic PDE system is guaranteed. Similar to the continuous time case, this control design is then subsequently used to develop a safe-parking framework for handling actuator faults in transport-reaction processes. The proposed framework is illustrated on a diffusion-reaction process</p>