Distributed Quasi-Nonlinear Model Predictive Control with Contractive Constraint

Distributed Quasi-Nonlinear Model Predictive Control by Dual Decomposition

A suboptimal approach to distributed NMPC for a class of systems consisting of nonlinear subsystems with linearly coupled dynamics subject to both state and input constraints is proposed. The approach applies a dual decomposition method to represent the original centralized NMPC problem into a distributed quasi-NMPC problem by linearization of the nonlinear system dynamics and taking into accou...

Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter

This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of ...

Model Predictive Control of Distributed Energy Resources with Predictive Set-Points for Grid-Connected Operation

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - poin...

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...