Robust Linear Parameter Varying Model Predictive Control and its Application to Wheel Slip Control

Robust shrinking ellipsoid model predictive control for linear parameter varying system

In this paper, a new off-line model predictive control strategy is presented for a kind of linear parameter varying system with polytopic uncertainty. A nest of shrinking ellipsoids is constructed by solving linear matrix inequality. By splitting the objective function into two parts, the proposed strategy moves most computations off-line. The on-line computation is only calculating the current...

Nonparametric Identification of Linearizations and Uncertainty using Gaussian Process Models – Application to Robust Wheel Slip Control

Gaussian process prior models offer a nonparametric approach to modelling unknown nonlinear systems from experimental data. These are flexible models which automatically adapt their model complexity to the available data, and which give not only mean predictions but also the variance of these predictions. A further advantage is the analytical derivation of derivatives of the model with respect ...

Improved Model Predictive Control and Its Application

* Corresponding author: [email protected] Abstract: This paper presents a modified form of model based predictive control that exploits the concept of non-trivial terminal state weighting in the cost function. While maintaining simplicity of implementation, the performance of this algorithm is not sacrificed and the computational burden compared with traditional approaches is greatly reduc...

Robust Control through Signal Constraints with Application to Predictive Control Robust Control through Signal Constraints with Application to Predictive Control

A Linear Matrix Inequality (LMI) Approach to Robust Model Predictive Control (RMPC) Design in Nonlinear Uncertain Systems Subjected to Control Input Constraint

In this paper, a robust model predictive control (MPC) algorithm is addressed for nonlinear uncertain systems in presence of the control input constraint. For achieving this goal, firstly, the additive and polytopic uncertainties are formulated in the nonlinear uncertain systems. Then, the control policy can be demonstrated as a state feedback control law in order to minimize a given cost funct...