In today’s hyper-competitive business environments virtual organisations are becoming highly dynamic and unpredictable. Individuals may want to work together across organisation boundaries but do not have much prior ...
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This paper describes the approach to collision avoidance problem for 3-DOF anthropomorphic robot manipulators. The novelty of the approach is the decomposition of 3D space to two 2D spaces. Resulting is the computatio...
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This paper describes the approach to collision avoidance problem for 3-DOF anthropomorphic robot manipulators. The novelty of the approach is the decomposition of 3D space to two 2D spaces. Resulting is the computationally efficient algorithm, suitable for implementation in the real-time systems. Simulation of the anthropomorphic manipulator operating in three dimensional space with obstacles is also presented.
A practical model of dynamic forecasting of urban ring road traffic flow based on neural network is proposed in this paper, and the traffic flow data of Beijing Third-Ring-Road (BTRR) are explored to test the validity...
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The paper presents the procedure for analytical determination of describing function of nonlinear element with fuzzy logic nonlinearity. The procedure is based on the method of analytical determination of describing f...
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We consider the constrained finite and infinite time optimal control problem for the class of discrete-time linear piecewise affine systems. When a linear performance index is used the finite and infinite time optimal...
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The control of dynamical systems with inherent non-linear characteristics has motivated research in non-linear control theory. Two main approaches to dealing with uncertainties in control systems design are adaptive a...
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The control of dynamical systems with inherent non-linear characteristics has motivated research in non-linear control theory. Two main approaches to dealing with uncertainties in control systems design are adaptive and robust control. In this paper, discrete direct adaptive control of unknown non-linear SISO systems is considered. The controller is implemented using a fuzzy neural network. The control concept is tested on a laboratory pilot plant and compared to a standard discrete PID Takahashi controller
In this paper we present a novel approach for the fault detection and diagnosis of nonlinear systems described by NARMA models. Firstly a known nonlinear system is considered, where an adaptive diagnostic model incorp...
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A practical model of dynamic forecasting of urban ring road traffic flow based on neural network is proposed in this paper, and the traffic flow data of Beijing Third-Ring-Road (BTRR) are explored to test the validity...
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A practical model of dynamic forecasting of urban ring road traffic flow based on neural network is proposed in this paper, and the traffic flow data of Beijing Third-Ring-Road (BTRR) are explored to test the validity of the model. The experimental results show that the forecasting data obtained by the model correspond to the actual data basically.
This paper deals with nonholonomic control systems subject to affine constraints. We first derive several preliminary properties of nonholonomic dynamic systems with affine constraints (NDSAC). We then investigate loc...
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This paper deals with nonholonomic control systems subject to affine constraints. We first derive several preliminary properties of nonholonomic dynamic systems with affine constraints (NDSAC). We then investigate local accessibility and local controllability of the NDSAC based on both Sussmann's theorem and linear approximation approaches. Conditions for local asymptotic stabilizability of the NDSAC by linear state feedback and nonlinear smooth state feedback are also derived. Finally, two physical examples are illustrated to confirm the results.
In this paper we present a novel approach for the fault detection and diagnosis of nonlinear systems described by NARMA models. Firstly a known nonlinear system is considered, where an adaptive diagnostic model incorp...
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In this paper we present a novel approach for the fault detection and diagnosis of nonlinear systems described by NARMA models. Firstly a known nonlinear system is considered, where an adaptive diagnostic model incorporating the estimate of fault is constructed. This has led to a new filtering design that either minimizes the residual entropy or controls the shape of the probability density function (PDF) of the residual. The diagnostic algorithm is then developed which produces the estimate of the fault so that the error between the system output and that of the model is minimized. Unknown nonlinear systems are then studied using a feedforward neural network trained to estimate the system under healthy conditions. Taking the trained neural network as the neuro-model of the system, similar detection and diagnostic algorithms to that of known systems are obtained.
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