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The DC-DC converters are used extensively in personal computers, computer peripherals, and adapters of consumer electronic devices to provide a fixed DC voltage. From the control viewpoint, the controller design must cope with their wide input voltage and load resistance variations to ensure the stability in any operating condition while providing fast transient response. For many years, the control...
Cerebellar model articulation controller (CMAC) has been already validated that it can approximate a nonlinear function over a domain of interest to any desired accuracy. This paper proposes an adaptive CMAC (PIACMAC) system with a PI-type learning algorithm. The PIACMAC system is composed of a CMAC and a compensation controller. CMAC is used to mimic an ideal controller and the compensation controller...
In this paper, a wavelet-neural-based adaptive control (WNBAC) with a PI type learning algorithm is proposed. The proposed WNBAC system is composed of a wavelet neural controller and a fuzzy compensation controller. The wavelet neural control is utilized to approximate an ideal controller and the fuzzy compensation controller with a fuzzy logic system in it is used to remove the chattering phenomena...
For many years, the control approaches for the dc-dc power converters are limited to PI controller structures. However, it gives the overshoot in output voltage as the rise time of response is reduced. To tackle this problem, an adaptive recurrent fuzzy neural network (ARENN) control system is developed in this paper. The on-line adaptive laws of the ARENN control scheme are derived based on the Lyapunov...
Since the dynamic characteristics of linear ultrasonic motor (LUSM) are highly nonlinear and time varying and the model is difficult to obtain, it is difficult to design a suitable controller to achieve high-precision position control by using the conventional control techniques. An intelligent tracking control system using an adaptive recurrent cerebellar model articulation controller (RCMAC) is...
This paper proposes an adaptive fuzzy sliding-mode controller with proportional-integral learning algorithm (PI-AFSMC) for the unknown nonlinear systems. All the controller parameters are on-line tuned by the derived learning algorithms in the Lyapunov stability theorem, thus the stability of the system can be guaranteed. Finally, the proposed PI-AFSMC is applied to control a linear piezoelectric...
This paper proposes an adaptive fuzzy controller (AFC) with a proportional-integral (PI) learning algorithm for an induction servomotor. The proposed AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is to mimic an ideal controller and the robust controller is to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All...
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