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This paper presents a new PID controller model with series leading correction. The controller has a tuning parameter, by tuning the parameter, the new PED controller can reduce the controlled system's maximum overshoot and setting time significantly, so as to improve the controlled system's stability and rapidity of step response. Simulation results show the superiority of the new PID controller.
A generalized PID algorithm (GPID) is proposed to improve the time response and control quality of the traditional PID control algorithm. The traditional PID controller is a special case of the generalized PID, this can be shown by comparing the coefficients of GPID and PID. GPID can be obtained by adding higher-order derivative terms in on the form of the traditional PID. GPID can get better control...
A direct adaptive control system for a class of unknown nonaffine discrete-time plants is introduced in this article. The proposed control law is constructed by the estimated system linearization with adjustable networks called muti-input fuzzy rules emulated networks or MIFRENs. Only on-line learning phase, the bounded parameters inside MIFRENs and the boundary of control error are given by the proposed...
The conventional internal model control and PID (IMC-PID) provides convenient tuning parameter to adjust the response speed and robustness of the closed-loop system because it has only one tuning parameter. But when the characteristics variation and uncertainty factors are included in the control system, it is difficult to accomplish satisfactory control performance by using conventional IMC-PID controllers...
PID control systems are widely used in many fields, and many methods to tune parameters of PID controller are known. When the characteristics of the object are changed, the traditional PID control should be adjusted by empirical knowledge. It may bring a worse performance to the system. In this paper, a new method to tune PID parameters called as the modified back propagate network by particle swarm...
A new class of adaptive nonlinear Hinfin control systems for nonlinear and time-varying processes which include nonlinear parametric models approximated by neural networks (NN), is proposed in this manuscript. Those control schemes are derived as solutions of particular nonlinear Hinfin control problems, where unknown system parameters, approximation and algorithmic errors in NN, and estimation errors...
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