Hybrid Predictive Control Research Articles

Hybrid predictive control of process systems

AbstractThe operation of chemical processes often requires respecting constraints on manipulated inputs and process states. Input constraints typically reflect limits on the capacity of control actuators, such as valves or pumps, whereas state constraints represent desirable ranges of operation for process variables, such as temperatures or concentrations. Constraints, however, limit the set of initial conditions, starting from where a process can be stabilized at a possibly open‐loop unstable steady state. Therefore, in control of constrained processes, it is important to obtain an explicit characterization of the region of closed‐loop stability. Model predictive control (MPC) provides a suitable framework for implementing control that respects manipulated input and process variable constraints while meeting prescribed performance objectives. Unfortunately, the implicit nature of the feedback law in MPC (the control action is computed by solving on‐line a constrained optimization problem at each sampling time) makes the a priori computation of the closed‐loop stability region a very difficult task. Such a computation, however, is possible when Lyapunov‐based bounded control techniques are used to design controllers for the stabilization of systems with manipulated input constraints. Motivated by this, the present work proposes a hybrid predictive control structure, which seamlessly unites MPC and bounded control, for output feedback stabilization of linear systems with input constraints. The design of the proposed structure is based on the idea of using a bounded controller, with its associated region of stability, as a “fallback” controller for the output feedback implementation of MPC. Switching laws are derived to orchestrate the transition between the two controllers in a way that reconciles their respective stability and optimality properties. The switched closed‐loop system is shown to possess a region of guaranteed stability, achieved by appropriately switching between the two controllers. The proposed hybrid control structure is shown to provide a safety net for the practical implementation of MPC by providing a priori knowledge, through off‐line computations, of a large set of initial conditions for which closed‐loop stability is guaranteed. Finally, two simulation studies are presented to demonstrate the implementation and evaluate the effectiveness of the proposed hybrid predictive control scheme, as well as test its robustness with respect to modeling errors and measurement noise. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1242–1259, 2004

Hybrid predictive control of nonlinear systems: method and applications to chemical processes

AbstractA hybrid control structure that unites bounded control with model predictive control (MPC) is proposed for the constrained stabilization of nonlinear systems. The structure consists of: (1) a finite‐horizon model predictive controller, which can be linear or nonlinear, and with or without stability constraints, (2) a family of bounded nonlinear controllers for which the regions of constrained closed‐loop stability are explicitly characterized and (3) a high‐level supervisor that orchestrates switching between MPC and the bounded controllers. The central idea is to embed the implementation of MPC within the stability regions of the bounded controllers and employ these controllers as fall‐back in the event that MPC is unable to achieve closed‐loop stability (due, for example, to infeasibility of a given initial condition and/or horizon length, or due to computational difficulties in solving the nonlinear optimization). Switching laws, that monitor the closed‐loop state evolution under MPC, are derived to orchestrate the transitions in a way that guarantees asymptotic closed‐loop stability for all initial conditions within the union of the stability regions of the bounded controllers. The proposed hybrid control scheme is shown to provide a paradigm for the safe implementation of predictive control algorithms to nonlinear systems, with guaranteed stability regions. The efficacy of the proposed approach is demonstrated through applications to chemical reactor and crystallization process examples. Copyright © 2004 John Wiley & Sons, Ltd.

A Predictive Controller for Automatic Voltage Regulation of DC Generators---A Hybrid Simulation Study

A hybrid predictive controller to maintain the plant output at the desired level is proposed. The controller consists of a fast-time linearized analog model of the controlled plant and a control logic, which is programmed on the digital section of the hybrid computer.

A hybrid predictive controller with an adaptive fast model†

A hybrid predictive controller is proposed for control of second-order, or higher-order systems which can be approximated to second order. The proposed controller uses hybrid computation and is suitable for on-line computer controlled problems. A fast time second-order adaptive model of the system is used in which the gain and one of the equivalent time constants of the system is calculated instantaneously. As an example the proposed controller is used for control of the output voltage of a dc. generating system under load variations. Responses are compared when the controller uses a system model with fixed parameters and when the model is made adaptive.