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This paper proposes an improved Finite Control Set Model Predictive Control (FCS-MPC) strategy to control static power converters that overcomes the parameter sensitivity of the conventional strategy. The parameters error can be due to both a poor estimation and their time variant behavior, as the grid parameters in grid connected static power converters. Contrary to the standard FCS-MPC approach...
This work explores the distributed model predictive control scheme. The Neighbor-Communication, Distributed Constrained-Model Predictive Control (NC-DCMPC) framework is suggested where a centralized problem is divided into dynamically coupled subsystems and the local controllers are allowed to communicate with neighbor agents only. The neighbor structure is defined based on the physical interconnections...
This paper presents an efficient computational method for solving the input-constrained, continuous time, infinite horizon, linear quadratic regulator problem to within a user specified tolerance. The infinite dimensional input trajectory is approximated with a piecewise linear function on a finite time discretization to ensure input constraint satisfaction. This approximate problem is then a standard...
This paper presents a computationally efficient model predictive control algorithm applicable for linear time varying systems with bounded disturbances. The proposed algorithm is based on the use of an optimized dynamic terminal control law that makes a good control performance possible with an online optimization over relatively smaller horizon lengths. In particular, the use of a dynamic terminal...
Model predictive control (MPC) applications habitually focus on large systems with slow dynamics. In addition, traditional constrained optimization techniques make use of algorithms not suited for fast MPC execution. This work suggests the use of conditional penalties in the optimization cost function to account for constraints. Such penalty functions resemble the barrier functions, where the penalty...
An efficient implementation of generalized predictive control using multi-layer feed forward neural network as the plantpsilas nonlinear model is presented. Two algorithm i.e. Newton Raphson and Levenberg Marquardt algorithm are implemented and their results are compared. The details about this implementation are given. The utility of each algorithm is outlined in the conclusion. In using Levenberg...
A dynamic matrix control with PID structure (PID-DMC) is derived by adding proportion, integral and differential structure to the conventional cost function. However the complicated inversion computation of higher dimensional matrix is involved in the PID-DMC and it restrains the on-line application. This paper is devoted to decreasing computational complexity by exploring its internal mechanism and...
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