In this paper the output-tracking problem of a class of composite nonlinear systems is studied. Composite system can be represented solely by models on the grounds of its inputs and measurable outputs. It is assumed t...
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In this paper the output-tracking problem of a class of composite nonlinear systems is studied. Composite system can be represented solely by models on the grounds of its inputs and measurable outputs. It is assumed that (i) the plant system belongs to aclass of composite non linear systems, and that the fuzzy logic control system applied can efficiently use adjustable parameters to approximate its non linear system function. The new theorem guarantees the synthesized indirect fuzzy adaptive control does ensure stable output tracking and possesses the ability to reduce the number of fuzzy rules.
In this article, an impedance model following force control, which uses a position/orientation compensator based on joystick-taught data, is proposed for an industrial robot with an open architecture controller. The p...
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An adaptive navigation control problem is presented for a quadruped robot in cluttered environments by incorporating the capability of adaptive resonance theory (ART) in stable category recognition into the fuzzy logi...
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A method for the identification of complex nonlinear dynamics of a multi-link robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs) in the absence of input torque information is proposed. The RKGNNs constr...
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As human on-off decisions are the basic problems in our human lives, the analysis of human on-off decision making is an interesting topic. The procedures of qualified human decision making include many intuitive facto...
As human on-off decisions are the basic problems in our human lives, the analysis of human on-off decision making is an interesting topic. The procedures of qualified human decision making include many intuitive factors which have been acquired from previous valuable experience and gained through learning, but they may not be easily understood by others within a short period. By the use of a database of causes and decisions made by qualified experts for an objective event, human decision making for that event can be realizable artificially. This paper investigates a general method for realizing artificial human on-off decision making based on the conditional probability of the database. As on-off decision making is a discrete event and the causes for that decision making are continuous events, a mathematical treatment of a Dirac delta function in a probability density function is required to derive the conditional probability for the decision making. Several examples of artificial human decision making by the proposed method were demonstrated, and the results obtained showed good agreement with those of human experts in the respective fields.
A method for the identification of complex nonlinear dynamics of a multilink robot manipulator using Runge-Kutta-Gill neural networks (RKGNN) in the absence of input torque information is proposed. The RKGNN construct...
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A method for the identification of complex nonlinear dynamics of a multilink robot manipulator using Runge-Kutta-Gill neural networks (RKGNN) in the absence of input torque information is proposed. The RKGNN constructed using shape adaptive radial basis functions (RBF) are trained using an evolutionary algorithm. Due to the fact that the main function network is divided into subnetworks to represent detailed properties of the dynamics of a manipulator, the neural networks have greater information processing capacity and they can be tested for properties such as positive definiteness of the inertia matrix. Dynamics of an industrial seven-link manipulator are identified using only input-output position and their velocity data. Promising experimental control results are obtained to prove the ability of the proposed method in capturing highly nonlinear dynamics of a multilink manipulator in an effective manner.
An adaptive navigation control problem is presented for a quadruped robot in cluttered environments by incorporating the capability of adaptive resonance theory (ART) in stable category recognition into the fuzzy logi...
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An adaptive navigation control problem is presented for a quadruped robot in cluttered environments by incorporating the capability of adaptive resonance theory (ART) in stable category recognition into the fuzzy logic control. An ART-based neural network is introduced as an environment identifier for the purpose of adaptive selection of the adequate rule base for the fuzzy controller. Therefore, the proposed adaptive control scheme for the robot navigation is implemented by the adaptive fuzzy rule base in response to changes of the robot's environment, which can be fine observed by the proposed environment identifier. Some simulation results are presented to illustrate the effectiveness of the proposed algorithm.
A method for the identification of complex nonlinear dynamics of a multi-link robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs) in the absence of input torque information is proposed. The RKGNNs constr...
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A method for the identification of complex nonlinear dynamics of a multi-link robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs) in the absence of input torque information is proposed. The RKGNNs constructed using shape adaptive radial basis functions are trained by an evolutionary algorithm. Due to the fact that the main function network is divided into sub-networks to represent the dynamic properties of the manipulator, the neural networks have greater information, processing capacity and can be tested for properties such as positive definiteness of the inertia matrix. Dynamics of a three-link manipulator are identified using only their input-output position and velocity data, and promising control results are obtained to prove the effectiveness of the proposed method in capturing highly nonlinear dynamics of a multi-link manipulator.
A number of neural network models and training procedures for time series prediction have been proposed in the technical literature. These models typically used uni-directional computation flow or its modifications. I...
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A number of neural network models and training procedures for time series prediction have been proposed in the technical literature. These models typically used uni-directional computation flow or its modifications. In this study a novel concept of bi-directional computation style is proposed and applied to prediction tasks. Since the coupling effects between the future prediction system and the past prediction system help the proposed model improve its performance, it is found that the prediction score is better than with the traditional uni-directional method. The bi-directional predicting architecture has been found to perform better than the conventional one when tested with standard benchmark sunspots data.
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