This paper considers an application of the decentralized adaptive output feedback scheme developed by Jain and Khorrami (1997) to maintain global robustness to parametric and dynamic uncertainties among interconnectio...
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This paper considers an application of the decentralized adaptive output feedback scheme developed by Jain and Khorrami (1997) to maintain global robustness to parametric and dynamic uncertainties among interconnections in large-scale power systems and also rejection of any bounded unmeasurable disturbances entering the system. The proposed design utilizes only feedback from local rotor angle measurements. The scheme is applied to a two-axis (fourth-order) model of the generator with a second-order turbine-governor model and an IEEE type I excitation control (fifth order) for each subsystem. The design presented is also applicable to higher-order models of the generator and turbine-governor system.
This paper presents a detailed analysis of a motion planner based on genetic algorithms for collision free motion planning of robotic manipulators through simulation. The problem is formulated for a 2-DOF planar manip...
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This paper presents a detailed analysis of a motion planner based on genetic algorithms for collision free motion planning of robotic manipulators through simulation. The problem is formulated for a 2-DOF planar manipulator moving in the presence of a static circular obstacle in its operational space. The algorithm is then extended to 3-DOF planar manipulator moving among multiple static obstacles.
This paper presents the fuzzy inference-based reinforcement learning algorithm of dynamic recurrent neural network, similar to the psychological learning scheme of the higher animals. The proposed method follows the w...
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ISBN:
(纸本)0780342089
This paper presents the fuzzy inference-based reinforcement learning algorithm of dynamic recurrent neural network, similar to the psychological learning scheme of the higher animals. The proposed method follows the way linguistic and conceptional expressions have an effect on human's behavior by reasoning reinforcement based on fuzzy rules. The intervals of fuzzy membership functions are found optimally by genetic algorithms. By using the recurrent neural network composed of dynamic neurons as action-generation network, not only the current state but also the past state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.
In this paper we describe the application of a robust adaptive control design to an ultra accurate (sub-micron) linear and planar step motor known as the Sawyer motor (1969). Simulation results presented show promisin...
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In this paper we describe the application of a robust adaptive control design to an ultra accurate (sub-micron) linear and planar step motor known as the Sawyer motor (1969). Simulation results presented show promising improvement in performance with the advocated controller.
In this paper, a robust adaptive nonlinear controller for various types of stepper motors is presented. The control design is applicable to both variable reluctance and permanent magnet stepper motors. It is also show...
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In this paper, a robust adaptive nonlinear controller for various types of stepper motors is presented. The control design is applicable to both variable reluctance and permanent magnet stepper motors. It is also shown that the methodology is applicable to other peculiar configurations of stepper motors (e.g. Sawyer motors). Furthermore, the motors may be either rotary or linear. To this end, a general electromechanical model of stepper motors is utilized. The model is comprised of the mechanical dynamics and electrical dynamics of the motor including the effects of cogging, viscous friction, winding resistance or other uncertainties. The robust adaptive design is based on our earlier work (1997) and does utilizes backstepping in the case that voltage level control is used rather than current controls. Simulation studies are presented to show the efficacy of the control design approach.
In this paper, nonlinear robust adaptive controller is proposed for friction compensation. The design has been performed for a six parameter dynamic model of friction. The advocated control design does not utilize any...
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In this paper, nonlinear robust adaptive controller is proposed for friction compensation. The design has been performed for a six parameter dynamic model of friction. The advocated control design does not utilize any knowledge of these six parameters and requires just an upper bound on the static friction level, which can be obtained experimentally. The controller is robust to dynamic uncertainties and can compensate the effect of friction which is separated from the control input through drive compliance. Simulation and experimental results are also presented.
In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. Thus th...
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In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying the immune system to DARS, a robot is regarded as a B lymphocyte (B cell), each environmental condition as an antigen and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows: when the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robot using communication. Finally, such stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis, and it is used for decision making of an optimal swarm strategy.
In this paper, we present the reinforcement learning and distributed genetic algorithm based behavior learning of the distributed autonomous mobile robots. The internal reinforcement signal for the reinforcement learn...
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In this paper, we present the reinforcement learning and distributed genetic algorithm based behavior learning of the distributed autonomous mobile robots. The internal reinforcement signal for the reinforcement learning is generated by fuzzy inference, and dynamic recurrent neural networks are used as action generation module. We adopt the distributed genetic algorithms for the cooperative behavior emergence. We show the validity of the proposed learning and evolution algorithm by computer simulation.
Application of robust adaptive nonlinear control to brushless DC motors is considered in this paper. The advocated robust adaptive controller enjoys robustness to parametric and dynamic uncertainties in the motor dyna...
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Application of robust adaptive nonlinear control to brushless DC motors is considered in this paper. The advocated robust adaptive controller enjoys robustness to parametric and dynamic uncertainties in the motor dynamics. In addition, the controller can reject any bounded disturbances acting on the motor. The controlled variables are phase voltages which are designed based on the backstepping technique. The closed-loop stability of the system is shown using Lyapunov techniques. The tracking errors are shown to be globally uniformly bounded. The design procedure is shown to be also applicable to multi-link manipulators actuated by brushless DC motors. The control design may be improved to achieve asymptotic tracking.
Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an imag...
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Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system.
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