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In this paper, a framework for embedding Model Predictive Control (MPC) for Systems-on-a-Chip applications is presented. This contribution is especially interesting when dealing with high performances devices since it increases the number of addressable control applications in the industrial field and particularly in fast systems. Aiming to allow the implementation of such a computationally expensive...
Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus,...
Considering the superiority of Divided Difference Filters (DDF) in state estimation of nonlinear systems versus conventional Extended Kalman Filter (EKF), DDF which are derivate-free Kalman filtering approach are exploited in state feedback control based on proportional integral (PI) controller. The proposed combination is applied to a Multi-Input Multi-Output three-tank system. The efficiency of...
This paper proposes a new mathematical method to solve min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Since the resulting optimization problem is non convex, a deterministic global optimization technique is adopted to solve it which is...
In this paper, a strategy for automatic tuning of predictive controller synthesis parameters based on multi-objective optimization (MOO) is proposed. This strategy integrates the genetic algorithm to generate the synthesis parameters (the prediction horizon, the control horizon and the cost weighting factor) making a compromise between closed loop performances (the overshoot, the variance of the control...
This paper deals with the robust predictive control of nonlinear systems. The behavior of the nonlinear system is described by an uncertainty Feedforward neural networks model, i.e. each output layer's parameter is uncertain. The control problem is formulated as a minimax optimization one which is a non convex problem. The performances of the proposed controller are illustrated and compared to a classical...
This work presents an application of linear and nonlinear robust predictive control onto a three tanks system. The design of the linear solution is based on Single-Input Single-Output Controlled Auto Regressive Integrated Moving Average (CARIMA) model and the nonlinear controller considers Nonlinear Auto Regressive with eXogenous output (NARX) model. Parametric uncertainties and polytopic uncertainties...
This paper provides an application of linear and nonlinear multivariable robust predictive control to a three tanks system. The design of the nonlinear solution is based on a Multi-Input Multi-Output Nonlinear Auto Regressive with eXogenous outputs (MIMO-NARX) model and the linear controller considers a MIMO Controlled Auto Regressive Integrated Moving Average (MIMO-CARIMA) model. Polytopic uncertainties...
This paper describes constrained multi objective predictive control of nonlinear systems. A nonlinear model based on the artificial neural networks (ANNs) is used to characterize the process at each operating point. The control law is provided by minimizing a set of control objective which is function of the future prediction output and the future control actions. Three aggregative methods are used...
This paper describes constrained multi objective predictive control of nonlinear systems. A nonlinear model based on the artificial neural networks (ANNs) is used to characterize the process at each operating point. The control law is provided by minimizing a set of control objective which is function of the future prediction output and the future control actions. Three aggregative methods are used...
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