The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Weiyen Wang - One of the best experts on this subject based on the ideXlab platform.
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observer based adaptive fuzzy neural control for unknown Nonlinear Dynamical Systems
Systems Man and Cybernetics, 1999Co-Authors: Yihguang Leu, Tsutian Lee, Weiyen WangAbstract:In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown Nonlinear Dynamical Systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the Nonlinear system are not assumed to be available for measurement. Also, the unknown Nonlinearities of the Nonlinear Dynamical Systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.
Gerasimos G. Rigatos - One of the best experts on this subject based on the ideXlab platform.
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A differential flatness theory approach to observer-based adaptive fuzzy control of MIMO Nonlinear Dynamical Systems
Nonlinear Dynamics, 2014Co-Authors: Gerasimos G. RigatosAbstract:The paper proposes a solution to the problem of observer-based adaptive fuzzy control for MIMO Nonlinear Dynamical Systems (e.g. robotic manipulators). An adaptive fuzzy controller is designed for a class of Nonlinear Systems, under the constraint that only the system’s output is measured and that the system’s model is unknown. The control algorithm aims at satisfying the $$H_\infty $$ H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the MIMO system into the canonical form, the resulting control inputs are shown to contain Nonlinear elements which depend on the system’s parameters. The Nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. Moreover, since only the system’s output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis, it is proven that the proposed observer-based adaptive fuzzy control scheme results in $$H_{\infty }$$ H ∞ tracking performance.
Yihguang Leu - One of the best experts on this subject based on the ideXlab platform.
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observer based adaptive fuzzy neural control for unknown Nonlinear Dynamical Systems
Systems Man and Cybernetics, 1999Co-Authors: Yihguang Leu, Tsutian Lee, Weiyen WangAbstract:In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown Nonlinear Dynamical Systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the Nonlinear system are not assumed to be available for measurement. Also, the unknown Nonlinearities of the Nonlinear Dynamical Systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.
Ying-cheng Lai - One of the best experts on this subject based on the ideXlab platform.
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Detection meeting control: Unstable steady states in high-dimensional Nonlinear Dynamical Systems
Physical review. E Statistical nonlinear and soft matter physics, 2015Co-Authors: Ying-cheng Lai, Wei LinAbstract:We articulate an adaptive and reference-free framework based on the principle of random switching to detect and control unstable steady states in high-dimensional Nonlinear Dynamical Systems, without requiring any a priori information about the system or about the target steady state. Starting from an arbitrary initial condition, a proper control signal finds the nearest unstable steady state adaptively and drives the system to it in finite time, regardless of the type of the steady state. We develop a mathematical analysis based on fast-slow manifold separation and Markov chain theory to validate the framework. Numerical demonstration of the control and detection principle using both classic chaotic Systems and models of biological and physical significance is provided.
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predicting catastrophes in Nonlinear Dynamical Systems by compressive sensing
Physical Review Letters, 2011Co-Authors: Wenxu Wang, Ying-cheng Lai, Rui Yang, Vassilios Kovanis, Celso GrebogiAbstract:An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in Nonlinear Dynamical Systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the Dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic Systems are provided to demonstrate our idea.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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model predictive control of unknown Nonlinear Dynamical Systems based on recurrent neural networks
IEEE Transactions on Industrial Electronics, 2012Co-Authors: Yunpeng Pan, Jun WangAbstract:In this paper, we present a neurodynamic approach to model predictive control (MPC) of unknown Nonlinear Dynamical Systems based on two recurrent neural networks (RNNs). The echo state network (ESN) and simplified dual network (SDN) are adopted for system identification and dynamic optimization, respectively. First, the unknown Nonlinear system is identified based on the ESN with input-output training and testing samples. Then, the resulting nonconvex optimization problem associated with Nonlinear MPC is decomposed via Taylor expansion. To estimate the higher order unknown term resulted from the decomposition, an online supervised learning algorithm is developed. Next, the SDN is applied for solving the relaxed convex optimization problem to compute the optimal control actions over the predicted horizon. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach. The proposed RNN-based approach has many desirable properties such as global convergence and low complexity. It is shown that the RNN-based Nonlinear MPC scheme is effective and potentially suitable for real-time MPC implementation in many applications.
