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Design of neuro-controller for complex dynamic systems is a big challenge faced by the researchers. In this paper we present a design of a robust neuro-controller for a dynamic system to make the system response fast with no overshoot. Here the control action decided by the controller completely depends on the value of the error at that point of time. The position feedback which controls the bandwidth...
Light weight robotic manipulators have significant advantages over their heavy counterparts in terms of efficiency of energy consumption and speed. However, they also have an inherent problem of vibrations which significantly affect their performance. Control strategies need to be developed to control their vibrational behavior. Many different control strategies have already been developed and tested...
Adaptive control theory has been recently used in damping the growing oscillations in power systems, enhancing the system response and improving system dynamic stability. This paper proposes a new robust adaptive control mechanism for FACTS. The adopted concept is the dead-zone modification based on model reference adaptive control (MRAC) technique. The developed controller aims to overcome the variation...
Switched reluctance motor can be used in many industrial applications. It is simple to construct and has salient pole stator and rotor. The rotor does not have any winding, commutators and brushes. This drives provide high reliability fast dynamic response and fault tolerance. But this SRM drives suffer from the disadvantage of having low power factor. The low power factor result in high losses in...
This paper considers the design of a decentralized adaptive approximation based control scheme for a class of interconnected nonlinear systems. Linearly parameterized neural networks are used to adaptively approximate the unknown dynamics of each subsystem and the unknown interconnections. The feedback control and adaptation laws are based only on local measurements of the state. A dead-zone modification...
On-line approximation based control methods seek improved performance for poorly modeled nonlinear systems by approximating the unknown nonlinearities as a part of the control design. Such on-line approximators cannot be perfect, so there would always remain some inherent approximation error. Recent articles have improved the robustness and convergence properties of such methods by also estimating...
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