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This paper presents an improved model predictive control (MPC) for permanent magnet synchronous motor (PMSM) drives. The proposed MPC is different from the conventional MPC in applying two voltage vectors in every control period. The duty ratio is firstly calculated using a simple but effective method with the consideration of the one-step delay. The cost function is then modified by incorporating...
This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN)...
Homotopy particle filters (HPFs), recently developed by Daum and Huang [1], present an alternative nonlinear filtering approach to sampling-based particle filters. Homotopy filters perform information update using the flow of particles to regions with high measurement likelihood. The particle flows in HPFs are solutions to a Fokker-Plank equation governing the dynamics of the posterior density function...
A coordinated control strategy is often used to ensure a thermal power plant to have a higher rate of load change, but without violating the thermal constraints. Although model predictive control has been widely used for controlling power plant, handling input constraints is a major problem especially as these plants are nonlinear. Two alternative methods of exploiting the nonlinear predictive control...
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