This paper proposes a design of internal model control systems for process with delay by using support vector regression(SVR).The proposed system fully uses the excellent nonlinear estimation performance of SVR with t...This paper proposes a design of internal model control systems for process with delay by using support vector regression(SVR).The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle.Closed-system stability and steady error are analyzed for the existence of modeling errors.The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.展开更多
This paper mainly aims at proposing an effective method of speed control of the low power motors like Permanent Magnet Direct Current (PMDC) motor used in the orthopedic surgeries using a natural optimization techniqu...This paper mainly aims at proposing an effective method of speed control of the low power motors like Permanent Magnet Direct Current (PMDC) motor used in the orthopedic surgeries using a natural optimization technique called genetic algorithm. Using this method, better values of Performance parameters like rise time, settling time, fall time, peak overshoot and steady state are achieved compared to the conventional PI controller. The SIMUINK MODEL of both the controller operation is obtained using MATLAB version R2013a. The simulated results reveal that the proposed control drive exhibits reduced peak overshoot, rise time, settling time and steady state error. An experimental setup is devised to validate the simulation results. The comparative analysis made depicts the superiority of the proposed algorithm with reference to its conventional counterpart.展开更多
文摘This paper proposes a design of internal model control systems for process with delay by using support vector regression(SVR).The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle.Closed-system stability and steady error are analyzed for the existence of modeling errors.The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.
文摘This paper mainly aims at proposing an effective method of speed control of the low power motors like Permanent Magnet Direct Current (PMDC) motor used in the orthopedic surgeries using a natural optimization technique called genetic algorithm. Using this method, better values of Performance parameters like rise time, settling time, fall time, peak overshoot and steady state are achieved compared to the conventional PI controller. The SIMUINK MODEL of both the controller operation is obtained using MATLAB version R2013a. The simulated results reveal that the proposed control drive exhibits reduced peak overshoot, rise time, settling time and steady state error. An experimental setup is devised to validate the simulation results. The comparative analysis made depicts the superiority of the proposed algorithm with reference to its conventional counterpart.
