New Minmax Linear Quadratic Fault-Tolerant Tracking Control for Batch Processes
Authors:
- 26 November 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 61 (10) , 3045-3051
Abstract
A new design of linear quadratic control (LQ) is proposed for batch processes with partial actuator faults and unknown disturbance. The autoregressive model built through input-output process data is transformed into a new non-minimal state space (NMSS) model in which the process state variables and tracking error are combined, then a new LQ control scheme is derived based on minimax optimization. Unlike LQ based on traditional NMSS model (TNMSSLQ), the process state variables and tracking error can both be regulated in the proposed method. Monotonic situations of optimal performance cost are further derived to yield sufficient conditions for both nominal and robust system stability. Finally, an injection velocity control example is illustrated to show the effectiveness.Keywords
Funding Information
- Zhejiang Provincial Natural Science Foundation of China (LR16F030004)
- National Natural Science Foundation of China (61273101)
- China National Funds for Distinguished Young Scientists (61425009)
This publication has 14 references indexed in Scilit:
- H∞ design of 2D controller for batch processes with uncertainties and interval time-varying delaysControl Engineering Practice, 2013
- Min–Max MPC based on an upper bound of the worst case cost with guaranteed stability. Application to a pilot plantJournal of Process Control, 2011
- A Unified Adaptive Iterative Learning Control Framework for Uncertain Nonlinear SystemsIEEE Transactions on Automatic Control, 2007
- An improved structure for model predictive control using non-minimal state space realisationJournal of Process Control, 2006
- Application of an explicit min-max MPC to a scaled laboratory processControl Engineering Practice, 2005
- Robust Dynamic Programming for Min–Max Model Predictive Control of Constrained Uncertain SystemsIEEE Transactions on Automatic Control, 2004
- A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejectionAutomatica, 2004
- Implementation of min–max MPC using hinging hyperplanes. Application to a heat exchangerControl Engineering Practice, 2004
- Trajectory generation for a UAV in urban terrain, using nonlinear MPC2001
- Quasi-Min-Max MPC algorithms for LPV systemsAutomatica, 2000