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Studies of model predictive control | |
Author | Vu Trieu Minh |
Call Number | AIT Diss. no.ISE-04-07 |
Subject(s) | Predictive control |
Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering, School of Advanced Technologies |
Publisher | Asian Institute of Technology |
Series Statement | Dissertation ; no. ISE-04-07 |
Abstract | Model Predictive Control (MPC) emerged recently in industry as a successful technique to deal with multivariable constrained control problems. MPC differs from other control algorithms in that the optimal control problem is solved on-line for the current state of the plant, rather than determined off-line as a feedback policy. The success of MPC control performance is highly dependent on the accuracy of the explicit process model. Thus, model errors can cause the system to become unstable or perform very poor. The goal of this research is to develop methods for improving MPC performance. The first part reviews a theoretical analysis of constrained linear MPC. A new algorithm for solving multi-parametric linear quadratic program with an explicit linear quadratic regulator is also presented in order to reduce on-line computations of linear quadratic optimal control problem subject to input and state constraints. The new method can be implemented off-line and allow better understand the MPC control action. The second part presents a modified MPC controller for ill-conditioned distillation process with output regions to improve the robustness of the controller for handling input and output constraints and rejecting disturbances. Compared to the traditional methods of MPC for ill-conditioned process, the modified method with output regions proves its ability to reject disturbance and maintain closed loop stability. The third part discusses moving horizon estimation (MHE) in MPC using state space models. In reality system states cannot be measured directly, therefore state estimators providing an estimate of the actual state, based on available measurements, are required to implement state feedback control schemes. In addition to MHE theoretical issues, practical algorithms for on-line calculation are also presented. The forth part develops a new nonlinear MPC scheme without a terminal constraint but with a terminal penalty adding into the objective function. Even though most of nonlinear MPC methods use a terminal constraint to ensure stability with a finite prediction horizon, the new method can also guarantee asymptotic closed-loop stability with both input and output constraints for difficult nonlinear systems. The fifth part of this work is to develop a new robust MPC controller for soften state constraints as penalty terms adding to the objective function. The new method accounts for model uncertainty in the controller design procedure. A min-max optimization problem is used to determine the optimal control action subject to input and output constraints. The last contribution of this thesis is to set up an interacting multiple model (IMM) based generalized predictive control (GPC) with effective adaptive algorithm for systems involving structural as well as parametric changes. The proposed approach provides an integrated framework for mode detection and state estimation for systems with uncertainties. |
Year | 2004 |
Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. ISE-04-07 |
Type | Dissertation |
School | School of Advanced Technologies (SAT) |
Department | Department of Industrial Systems Engineering (DISE) |
Academic Program/FoS | Industrial Systems Engineering (ISE) |
Chairperson(s) | Afzulpurkar, Nitin V.; |
Examination Committee(s) | Manukid Pamichkun; Huynh Ngoc Phien;McAREE, Peter Ross; |
Scholarship Donor(s) | Government of Austria; |
Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2004 |