With the widespread availability of model predictive control (MPC), nonlinear MPC provides a natural extension to include nonlinear models for trajectory tracking and dynamic optimization. NMPC can include first principle models developed for off-line dynamic studies as well as nonlinear data-driven models, but requires the application of efficient large-scale optimization strategies to avoid c...

This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the pas...

This paper presents a weight optimization method for nonlinear model predictive controller (NMPC) based on the genetic algorithm (GA) ship trajectory tracking. The coefficients Q and R of objective function in NMPC are obtained via real-time instead trial error method, which improves efficiency accuracy controller. In addition, targeted improvements made to internal crossover operator, mutation...

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear statespace form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentia...

The mechanical simplicity, hover capabilities, and high agility of quadrotors lead to a fast adaption in the industry for inspection, exploration, urban aerial mobility. On other hand, unstable underactuated dynamics render them highly susceptible system faults, especially rotor failures. In this work, we propose fault-tolerant controller using nonlinear model predictive control (NMPC) stabiliz...

A recurrent neural network (RNN) and differential evolution optimization (DEO) based nonlinear model predictive control (NMPC) technique is proposed for position of a single-link flexible-joint (FJ) robot. First, simple three-layer with rectified linear units as an activation function (ReLU-RNN) employed approximating the system dynamic model. Then, using RNN (MPC) scheme, DEO NMPC controller d...

-A novel approach for the implementation of Nonlinear Model Predictive Control (NMPC) using Particle Swarm Optimization (PSO) technique is proposed. Two different approaches are made in the PSO algorithms, Random PSO (RPSO) and knowledge based PSO (KPSO) for the determination of optimum controller gain in MPC structure In order to test the performance of the proposed PSO based MPC system a nonl...

Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gas emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the Nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion control. Recently, an explicit approximate ...

In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear model predictive control (NMPC) approach, for DFIG-based wind turbine. The DFIG is fed through the rotor windings by a back-to-back converter controlled by Pulse Width Modulation (PWM), where the stator winding...