This paper looks into the determination of optimal trajectories of a nonlinear model of a two-link articulated manipulator. In a first step, genetic algorithms are used to generate an optimal control sequence which is...
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This paper looks into the determination of optimal trajectories of a nonlinear model of a two-link articulated manipulator. In a first step, genetic algorithms are used to generate an optimal control sequence which is used to bring the manipulator robot into a desired position. In a second step, genetic algorithms optimize the parameters of membership functions to facilitate the realization of a Sugeno fuzzy logic based optimal controller. Simulation results show that the second step gives suboptimal solutions, however the first step yields to optimal solutions which are very sensitive with respect to the parameter variation of the system.
作者:
A. OuezriN. DerbelIntelligent Control
decision & Optimisation of complex Systems Research Unit (ICOS) National School of Engineering of Sfax (ENIS) University of Sfax Sfax Tunisia
This paper deals with the determination of the optimal control strategies of a rotary crane. Such system is represented by a sixth order nonlinear mathematical model. The proposed idea is based on the decomposition of...
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This paper deals with the determination of the optimal control strategies of a rotary crane. Such system is represented by a sixth order nonlinear mathematical model. The proposed idea is based on the decomposition of the system into two interconnected subsystems which are linear with respect to their state and control vectors. The nonlinearities are located in the interconnexion terms. In this context, local feedback gains can be computed by solving a nonlinear Riccati equation, whereas, the local gains depend on the state vector of the other subsystem. Within this approach, a fuzzy logic system and a neural network have been constructed in order to identify these gains. Simulation results show the high performances of the proposals.
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