Towards Parallelizable Sampling–based Nonlinear Model Predictive Control

Towards parallelizable sampling-based Nonlinear Model Predictive Control

This paper proposes a new sampling–based nonlinear model predictive control (MPC) algorithm, with a bound on complexity quadratic in the prediction horizonN and linear in the number of samples. The idea of the proposed algorithm is to use the sequence of predicted inputs from the previous time step as a warm start, and to iteratively update this sequence by changing its elements one by one, sta...

Nonlinear Model Predictive Control

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Towards a Sampled-Data Theory for Nonlinear Model Predictive Control

This paper considers the stability, robustness and output feedback problem for sampled-data nonlinear model predictive control (NMPC). Sampleddata NMPC here refers to the repeated application of input trajectories that are obtained from the solution of an open-loop optimal control problem at discrete sampling instants. Specifically we show that, under the assumption that the value function is c...

Nonparametric Nonlinear Model Predictive Control

−Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction ...