We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of proposed is that application does not require solution classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, formulation simply minimizes predicted error, possibly with some input regularization. Instead us...

Acknowledgments I would like to express my appreciation to my advisor, Dr. James B. Rawl-ings. His diicult questions have pushed this work forward when it stalled and his insight has allowed me to progress when it seemed impossible. His vision of computing in this profession has provided his research group with the best computer facilities at the University. The work reported in this dis-sertat...

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost function in terms of expected values and higher moments of the states, and chance constraints that ensure probabilistic constraint satisfaction. The general...

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...

In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant...

Among the several problems arising in the Airborne Wind Energy paradigm, an essential one is the control of the tethered airfoil trajectory during power generation. Tethered flight is a fast, strongly nonlinear, unstable and constrained process, motivating control approaches based on fast Non-linear Model Predictive Control. In this paper, a computationally efficient model is proposed, based on...

A nonlinear chemical/physical dynamic model in the form of partial differential equations was adopted and further developed to serve as a basis for model predictive control of a pellet reactor for drinking water softening. The model was calibrated using full-scale process measurements. A linear predictive controller based on a lineralization of the model has been designed to achieve the desired...

This paper describes an efficient approach for nonlinear model predictive control, applied to a 6-DOF arm robot. The model is first linearized and decoupled by feedback, secondly a model predictive control scheme, implemented with an optimized dynamic model and running within small sampling period, is exhibited. Major simulation results performed using numerical values of an industrial PUMA 560...