This paper deals with the real time control of an electromagnetic ball suspension system using multiple model based predictive control (MMPC). The objective is to control the position of a metal ball in air space. The multiple model predictive control is designed based on the linearized models of the system about multiple operating conditions. For real time control a laboratory version of magnetic levitation device (33-210) by Feedback instruments is used. The performance of MMPC is compared with normal model predictive control (MPC) and found that MMPC is better than MPC.
Most of industrial process can be approximately represented as first-order plus delay time (FOPDT) model or second-order plus delay time (FOPDT) model. From a control point of view, it is important to estimate the FOPDT or SOPDT model parameters from arbitrary process input as groomed test like step test is not always feasible. Orthonormal basis function (OBF) are class of model structure having many advantages, and its parameters can be estimated from arbitrary input data. The OBF model filters are functions of poles and hence accuracy of the model depends on the accuracy of the poles. In this paper, a simple and standard particle swarm optimisation technique is first employed to estimate the dominant discrete poles from arbitrary input and corresponding process output. Time constant of first order system or period of oscillation and damping ratio of second order system is calculated from the dominant poles. From the step response of the developed OBF model, time delay and steady state gain are estimated. The parameter accuracy is improved by employing an iterative scheme. Numerical examples are provided to show the accuracy of the proposed method.
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