Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442613
Title: Softly switched model predictive control : generic development and application to water supply and distribution systems
Author: Wang, Jingsong.
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2006
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Abstract:
This thesis is concerned with the softly switched model predictive control (SS-MPC) theory and its application to optimal operation of water supply and distribution systems. For systems having control input saturation, a softly switched model predictive control scheme with two specific methods is proposed in order to achieve better switching transients than MPC controller hard switching. Stability properties of the SS-MPC are investigated and sufficient asymptotic stabilization conditions are derived. Taking state constraints and system uncertainty into account, a robustly feasible soft switching process is designed by bridging the old and new MPC controllers through a sequence of linked invariant sets. An invariant set based fast switching method is also developed in order to complete the switching process as fast as possible in the case of infeasible MPC controller hard switching while sacrificing the switching transients. The developed SS-MPC methodology is extended to the scope of hybrid predictive control, where a class of nonlinear hybrid dynamical systems are considered. For operational control purposes, a piecewise affine modelling method for water supply and distribution systems is proposed. The proposed control model enables predictive control strategies to be formulated as mixed integer linear inequalities and ensures that the obtained optimization solutions are global optima. The effectiveness of the proposed control modelling method and the softly switched model predictive control mechanism are finally verified by applying them to a simulated benchmark water supply and distribution network.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.442613  DOI: Not available
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