Description
Taylor & Francis Ltd Modern Predictive Control 2009 Edition by Ding Baocang
Modern Predictive Control explains how MPC differs from other control methods in its implementation of a control action. Most importantly, MPC provides the flexibility to act while optimizing-which is essential to the solution of many engineering problems in complex plants, where exact modeling is impossible. The superiority of MPC is in its numerical solution. Usually, MPC is employed to solve a finite-horizon optimal control problem at each sampling instant and obtain control actions for both the present time and a future period. However, only the current control move is applied to the plant.This complete, step-by-step exploration of various approaches to MPC:Introduces basic concepts of systems, modeling, and predictive control, detailing development from classical MPC to synthesis approachesExplores use of Model Algorithmic Control (MAC), Dynamic Matrix Control (DMC), Generalized Predictive Control (GPC), and Two-Step Model Predictive Control Identifies important general approaches to synthesisDiscusses open-loop and closed-loop optimization in synthesis approachesCovers output feedback synthesis approaches with and without a finite switching horizonThis book gives researchers a variety of models for use with one- and two-step control. The author clearly explains the variations between predictive control methods-and the root of these differences-to illustrate that there is no one ideal MPC and that one should remain open to selecting the best possible model in each unique circumstance. Systems, modeling and model predictive controlSystemsModelingState space model and input/output modelDiscretization of continuous-time systems Model predictive control (MPC) and its basic propertiesThree typical optimal control problems of MPC Finite-horizon control: an example based on "three principles" Infinite-horizon control: an example of dual-mode suboptimal control Development from classical MPC to synthesis approaches Model algorithmic control (MAC) Principle of MAC Constraint handlingThe usual pattern for implementation of MPC Dynamic matrix control (DMC) Step response model and its identification Principle of DMC Constraint handlingGeneralized predictive control (GPC) Principle of GPC Some basic properties Stability results not related to the concrete model coefficients Cases of multivariable systems and constrained systemsGPC with terminal equality constraint Two-step model predictive control Two-step GPC Stability of two-step GPC Region of attraction by using two-step GPCTwo-step state feedback MPC (TSMPC)Stability of TSMPCDesign of the region of attraction of TSMPC based on semiglobal stability Two-step output feedback model predictive control (TSOFMPC)Stability of TSOFMPCTSOFMPC: case where the intermediate variable is available Sketch of synthesis approaches of MPC General idea: case discrete-time systemsGeneral idea: case continuous-time systemsRealizationsGeneral idea: case uncertain systems (robust MPC) Robust MPC based on closed-loop optimization A concrete realization: case continuous-time nominal systems State feedback synthesis approaches System with polytopic description, linear matrix inequalityOn-line approach based on min-max performance cost: case zero-horizonOff-line approach based on min-max performance cost: case zero-horizonOff-line approach based on min-max performance cost: case varying-horizon Off-line approach based on nominal performance cost: case zero-horizonOff-line approach based on nominal performance cost: case varying-horizon Synthesis approaches with finite switching horizon Standard approach for nominal systemsOptimal solution to infinite-horizon constrained linear quadratic control utilizing synthesis approach of MPCOn-line approach for nominal systems Quasi-optimal solution to the infinite-horizon constrained linear time-varying quadratic regulation utilizing synthesis approach of MPC On-line approach for systems with polytopic descriptionParameter-dependent on-line approach for systems with polytopic description Open-loop optimization and closed-loop optimization in synthesis approaches A simple approach based on partial closed-loop optimization Triple-mode approach Mixed approach Approach based on single-valued open-loop optimization and its deficiencies Approach based on parameter-dependent open-loop optimization and its properties Approach with unit switching horizon Output feedback synthesis approaches Optimization problem: case systems with input-output (I/O) nonlinearities Conditions for stability and feasibility: case systems with I/O nonlinearities Realization algorithm: case systems with I/O nonlinearitiesOptimization problem: case systems with polytopic description Optimality, invariance and constraint handling: case systems with polytopic description Realization algorithm: case systems with polytopic description Bibliography Index