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This paper describes two methodologies for implementation of Hammerstein model by using different input-output representations into model predictive control schemes. The model nonlinearity is easily approximated using a simple Takagi-Sugeno inference, while the linear parts are flexibly introduced. As optimization procedures for predictive control are used a standard gradient optimization method and...
In this study issues related to applicability of Model-Based Predictive Control (MBPC) to nonlinear and complex processes are addressed. A tank system is taken as an exemplary process, and its prediction model is used for control purposes. Obtained results are applied for level control of a tank process. A Takagi-Sugeno type fuzzy neural network is used to model the nonlinear system. The obtained...
A recently developed method for real-time hybrid model predictive control (HMPC) of switching converters is extended to the Ćuk converter. Specifically, an integral quadratic performance index is used for stored energy and power flow regulation. The discrete-time HMPC problem is solved in real time using an active set optimization. Experimental results show that the control performs well in both...
This paper presents model predictive control method for load shedding based on trajectory sensitivities. According to the basic principle of model predictive control, aiming at minimizing the quadric performance index which comprehensively considers both the control cost and the deviation between bus voltage predictive trajectories and their reference values, the receding optimization model is described...
In this paper, model predictive control is applied to an inverted pendulum apparatus and the effect of input disturbance are studied. The optimization problem is solved on-line using quadratic programming approach on a PC hardware platform.
This paper deals with the problem of nonlinear model predictive control using a piecewise linear predictive model: the adaptive hinging hyperplanes (AHH). The AHH model is adaptive and efficient, thus depicts the relationship of the nonlinear system very well. Thanks to the piecewise linear property of the predictive model, a series of convex quadratic programming are constructed in the controller...
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