Learning-based nonlinear model predictive control with accurate uncertainty compensation

Improving the stability of the power system based on static synchronous series compensation equipped with robust model predictive control

Low-frequency oscillations (LFO) imperil the stability of the power system and reduce the Capacity of transmission lines. In the power systems, FACTS devices and Power System stabilizers are used to improve the stability. Static synchronous series compensators is one of the most important FACTS devices. This paper investigates the damping of LFO with static synchronous series compensator (SSSC)...

Tube - Based Robust Nonlinear Model Predictive Control

A successful method for model predictive control of constrained linear systems uses a local linear control law that, in the presence of disturbances, maintains the disturbed trajectory of the controlled system in a tube whose centre is the nominal trajectory (generated ignoring the disturbance) and whose ‘cross-section’ is a robust, positively invariant set; robust exponential stability of an i...

Passivity-based Nonlinear Model Predictive Control

In this thesis a nonlinear model predictive control (NMPC) scheme is presented that is based on the concept of passivity. The proposed NMPC scheme takes advantage of the relationship between optimal control and passivity and the relationship between optimal control and model predictive control. This novel NMPC scheme, guaranteeing closedloop stability through the incorporation of a passivity-ba...

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...