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In this paper, a sensorless neural network speed control strategy of permanent magnet synchronous machines (PMSMs) is introduced as an alternative to conventional control techniques. The control strategy achieves accurate tracking by making use of artificial neural network (ANN) learning capabilities to approximate the machine's nonlinear dynamics. The ANN controller's output is then fed to a Space...
In this paper, an artificial neural network (ANN) based control scheme is introduced for the inverted pendulum motion and posture control problem. The adaptive control strategy consists of a Lyapunov stability-based online weights adaptation that provides asymptotic tracking while learning the nonlinear inverted pendulum system's dynamics. Unlike other control strategies, no a priori offline training,...
This paper describes predictive control of pneumatic actuator. Pneumatic cylinder is a complex nonlinear object because of friction and compressibility of the air. A precision and fast control such an object using traditional methods of control is very difficult. Predictive control with neural networks model of the plant is one of the modern approaches to control complex nonlinear objects.
In this paper, a grey neuro-adaptive control algorithm is suggested for Antilock Braking Systems (ABS). The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. A multilayer neural network and a grey predictor, GM(1,1) model, are combined in the approach proposed in the paper. The grey neural network controller is...
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