Nonlinear predictive control for real time applications
ABSTRACT The design of nonlinear predictive controllers based on linear time-varying prediction models is discussed. The linear time-varying models can be obtained by applying a local linearization along the nominal input and state trajectory or by describing the nonlinear state equations by state dependent state space equations. A graphical predictive control framework that provides practical methods for nonlinear control design is introduced using LabVIEW. The effectiveness of the algorithms and the easy applicability of the developed framework are illustrated in a simulation example
Conference Paper: Nonlinear predictive control of an industrial power plant boiler[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes an efficient nonlinear predictive controller (NMPC) for application to a power plant drum-boiler. The control objectives are to maintain the water and pressure levels in the drum within a desired range. First, the nonlinear model of the drum-boiler is transformed to LTV state-dependent nonlinear form to provide global nonlinear behavior. Next, state-dependent nonlinear Kalman filter is used to estimate the system states. Then, a supervisory NMPC algorithm is used as a second level controller to generate optimal set points to the lower level regulating PID loops while maintaing output constraints. Simulation results are presented to demonstrate the excellent tracking and disturbance rejection performance compared with a stand-alone multi loop PID controllers.Control & Automation (MED), 2012 20th Mediterranean Conference on; 01/2012
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ABSTRACT: In this paper, a new incremental predictive guidance method b ased on implicit form of velocity to be gained algorithm is proposed. In this approach, the generalized incremental predictive control (GIPC) approach is applied to the linearized model to compensate for the guidance error. Instead of using the present state in popular model based predictive controller (MPC), in the new method b oth previous and present states are utilized. GIPC approach introduces a f e edback action including the weighted diierence of the process states and the summation of the control action increments. To evaluate the robustness and performance of the proposed approach, the parameter uncertainties of the guidance and control are considered and a comparison with standard GPC is performed by extensive computer simulations. The results show a signiicant improvement in the robustness as well as tracking performance of the perturbed initial value of velocity to be gained or the reference signal.