Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

Abstract

In process industry, there exist many systems which can be approximated by block-oriented nonlinear models, including Hammerstein and Wiener models. Hammerstein model consists of the cascade connection of a static (memoryless) nonlinear block followed by a dynamic linear block while Wiener model the reverse. Moreover, these systems are usually subjected to input constraints, which makes the control of block-oriented nonlinearities challenging. In this chapter, a Multi-Channel Identification Algorithm (MCIA) for Hammerstein systems is first proposed, in which the coefficient parameters are identified by least squares estimation (LSE) together with singular value decomposition (SVD) technique. Compared with traditional single-channel identification algorithms, the present method can enhance the approximation accuracy remarkably, and provide consistent estimates even in the presence of colored output noises under relatively weak assumptions on the persistent excitation (PE) condition of the inputs. Then, to facilitate the following controller design, the aforementioned MCIA is converted into a Two Stage Single-Channel Identification Algorithm (TS-SCIA), which preserves most of the advantages of MCIA. With this TS-SCIA as the inner model, a dual-mode Nonlinear Model Predictive Control (NMPC) algorithm is developed. In detail, over a finite horizon, an optimal input profile found by solving a open-loop optimal control problem drives the nonlinear system state into the terminal invariant set, afterwards a linear output-feedback controller steer the state to the origin asymptotically. In contrast to the traditional algorithms, the presentmethod has amaximal stable region, a better steady-state performance and a lower computational complexity. Finally, a case study on a heat exchanger is presented to show the efficiency of both the identification and the control algorithms. On the other hand, for Wiener systems with input constraints, since most of the existing control algorithms cannot guarantee to have sufficiently large regions of asymptotic stability, we adopted a subspace method to separate the nonlinear and linear blocks in a constrained multi-input/multi-output (MIMO) Wiener system and then developed a novel dual-mode