Branch-and-bound Optimization in Multivariable Model Predictive Control
نویسندگان
چکیده
The branch-and-bound optimization method is a discrete search technique, which has been successfully applied to different complex optimization problems. This method can be applied to model predictive control when the control actions are discretized. This paper generalizes the aplication of branch-and-bound in model predictive control to multivariable systems. New features have been introduced to extend the application of branch-and-bound to MIMO systems. A realistic simulation of a gantry crane shows the applicability of the method. Copyright c Controlo’2000
منابع مشابه
Adaptive Fuzzy Model Predictive Control for non-minimum phase and uncertain dynamical nonlinear systems
this paper introduces a method to design a robust adaptive predictive control based on Fuzzy model. The plant to be used as predictive model is simulated by TakagiSugeno Fuzzy Model, and the optimization problem is solved by a Genetic Algorithms or Branch and Bound. The method to tune parameters of the model predictive controller based on Lyapunov stability theorem is presented in this paper to...
متن کاملPerformance Comparison of Predictive Controllers in Optimal and Stable Operation of Wastewater Treatment Plants
Any proper operation could be translated as a constrained optimization problem inside a WWTP, whose nonlinear behavior renders its control problems quite attractive for performance of multivariable optimization–based control technique algorithms, such as NMPC. The main advantage of this control technique lies in its ability to handle model nonlinearity as well as various types of constraints on...
متن کاملPerformance Comparison of Predictive Controllers in Optimal and Stable Operation of Wastewater Treatment Plants
Any proper operation could be translated as a constrained optimization problem inside a WWTP, whose nonlinear behavior renders its control problems quite attractive for performance of multivariable optimization–based control technique algorithms, such as NMPC. The main advantage of this control technique lies in its ability to handle model nonlinearity as well as various types of constraints on...
متن کاملAnalysis of Applying Event-triggered Strategy on the Model Predictive Control
In this paper, the event-triggered strategy in the case of finite-horizon model predictive control (MPC) is studied and its advantages over the input to state stability (ISS) Lyapunov based triggering rule is discussed. In the MPC triggering rule, all the state trajectories in the receding horizon are considered to obtain the triggering rule. Clearly, the finite horizon MPC is sub-optimal with ...
متن کاملDesigning a novel structure of explicit model predictive control with application in a buck converter system
This paper proposes a novel structure of model predictive control algorithm for piecewise affine systems as a particular class of hybrid systems. Due to the time consuming and computational complexity of online optimization problem in MPC algorithm, the explicit form of MPC which is called Explicit MPC (EMPC) is applied in order to control of buck converter. Since the EMPC solves the optimizati...
متن کامل