Event-triggered intermittent sampling for nonlinear model predictive control

Event-triggered intermittent sampling for nonlinear model predictive control

In this paper, we propose a new aperiodic formulation of model predictive control for nonlinear continuous-time systems. Unlike earlier approaches, we provide event-triggered conditions without using the optimal cost as a Lyapunov function candidate. Instead, we evaluate the time interval when the optimal state trajectory enters a local set around the origin. The obtained event-triggered strate...

Analysis 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 ...

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 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