Font Size: a A A

Study On LMI Based Predictive Control For Systems With Time-delay

Posted on:2009-09-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:M LuFull Text:PDF
GTID:1118360275454598Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Model predictive control, which is a control algorithm able to handle constrained complex systems, has been widely used in process industry. When the predictive control algorithm was used to control systems with time-delay, two kinds of methods were applied. Firstly, the optimization horizon was d larger than that of algorithm which was used to control systems without time-delay. This method was adapted to time-delay systems with pure time delay. Secondly, systems with time-delay were transformed into systems without time-delay. As a result, the computation burden was increased. There always exist phenomena such as uncertainties, nonlinearity, time-delay and disturbances in industry. This dissertation investigates systematically predictive control algorithms taking time-delay and uncertainties into consideration explicitly in the course of controller design for systems with time-delay.The main contents of this dissertation are stated as follows:1. Saturating state feedback predictive control algorithms are investigated for norm-bounded and polytopic uncertain systems with state delay subject to input saturation. When polytopic uncertainty is considered, constant state feedback and gain-scheduling predictive control algorithms are presented. The latter can obtain better performance, but computation burden is high. To overcome this drawback, a predictive control algorithm based on nominal objective function is presented. For polytopic uncertain systems with both state and input delays, a saturating state feedback predictive control algorithm is also investigated. The simulation results demonstrate that the algorithms are effective.2. Finite horizon fuzzy predictive control algorithms for nonlinear systems with both state and input delays subject to input constraints are investigated. The predictive control algorithms applying common Lyapunov-Krasovskii function as the terminal cost function, applying fuzzy state feedback controller and fuzzy saturated controller as local controllers are presented. To improve the performance of the closed-loop system, a fuzzy Lyapunov-Krasovskii function is applied as the terminal cost function. From the application to continuous stirred tank reactor (CSTR), the effectiveness of the algorithms is guaranteed. And the system controlled by the algorithm whose terminal cost function is a fuzzy Lyapunov-Krasovskii function can obtain better performance.3. Non-fragile state feedback predictive control algorithm for piecewise system with state delay is investigated. Errors in the controller coefficients which always exist in industry speed the design of non-fragile controllers. In the algorithm, the controlled system is extended into uncertain piecewise linear system with state delay and nonlinear fuzzy system with state delay, piecewise Lyapunov-Krasovskii function is applied and both additive and multiplicative types of controller perturbations are considered. Simulation results show that the presented algorithms are effective.4. Receding horizon H∞control algorithms are investigated for systems with state delay subject to input constraints and disturbances. Combining receding optimization of model predictive control and H∞control, the algorithms are a LMI-based receding optimization problem with new measurement at each time instant. The disturbance attenuation levels of the controlled systems are guaranteed and the input constraints are satisfied. When not all states are available, a dynamic output feedback receding horizon H∞control algorithm is presented. The simulation verifies that both state feedback and dynamic output feedback receding horizon H∞control algorithms are effective to control the systems with state delay subject to input constraints and disturbances.
Keywords/Search Tags:model predictive control, robust control, system with time-delay, fuzzy system, input saturation, non-fragile control, uncertainty, nonlinear
PDF Full Text Request
Related items