41 research outputs found

    Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach

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    10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN

    A brief review of neural networks based learning and control and their applications for robots

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    As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

    Tracking control of a marine surface vessel with full-state constraints

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    Adaptive control and neural network control of nonlinear discrete-time systems

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    Ph.DDOCTOR OF PHILOSOPH

    Finite-Time Sliding Mode Control Design for Unknown Nonaffine Pure-Feedback Systems

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    Model Identification and Control Design for a Humanoid Robot

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    In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design

    Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

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    A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances
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