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This paper first establishes the kinematic model and dynamic model of Selective Compliance Assembly Robot Arm(SCARA) robot based on Denavit-Hartenberg method and Lagrange equation. Then the model is simplified to reduce the computation, the kinetic equation is transformed into a linear form to get the observation matrix and the parameters to be identified. An incentive trajectory is designed to finish...
A control scheme combining novel model reference adaptive control (MRAC) and neural network (NN) is proposed in this paper to achieve high tracking precision for servo systems. This scheme comprises an MRAC controller and an online NN controller in the velocity-loop and a traditional PD controller in the position-loop. For reducing influences which arose from modeling error, unknown model dynamics,...
In this paper, a novel control scheme based on RBF Neural Network is proposed for High-precision Servo System. The aim of this study is to reduce the influence which arises from modeling error, unknown model dynamics, parameter variation and disturbance acted on the practical system and to achieve high tracking precision. This scheme consists of a Neural Network controller (NNC), a Feedforward controller...
This paper discusses adaptive control method used in high performance servo system based on Cerebellar Model Articulation Controller (CMAC). It analyzes the uncertain and nonlinear factors of the servo system, studies the modeling method using the frequency property of the system, and then uses an additional item to instead of the non-modeling factor. In the proposed control scheme, the CMAC controller...
This paper developed a control scheme of neural network based on feedforward and PD (proportional and derivative) control for high-precision flight simulator. A radial basis-function neural network (RBFNN) controller was used to learn and to compensate the unknown model dynamics, parameter variation and disturbance of the system on-line. The iterative algorithm of RBFNN parameters is got by Lyapunov...
This paper developed a model reference control scheme by introducing a PI controller and CMAC neural network (CMACNN) controller for speed control of high precision servo systems. It contemporarily improved the conception mapping algorithm of the CMACNN, which gave a determined expression of the physical memory size and designed a physical memory address function. In the proposed control scheme, the...
In this paper, a novel model reference adaptive control (MRAC) scheme based on neural network (NN) is proposed for servo system tracking control to achieve high-precision position control. This scheme consists of an MRAC controller and an online NN controller in velocity-loop and a traditional PID controller in position-loop. For reducing influence which arose from modeling error, unknown model dynamics,...
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