The Experts below are selected from a list of 3870 Experts worldwide ranked by ideXlab platform
Peng Shi - One of the best experts on this subject based on the ideXlab platform.
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command filtering based fuzzy control for nonlinear systems with Saturation input
IEEE Transactions on Systems Man and Cybernetics, 2017Co-Authors: Peng Shi, Wenjie Dong, Chong LinAbstract:In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with Saturation Nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.
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strict dissipativity and asymptotic stability of digital filters in direct form with Saturation Nonlinearity
Nonlinear Dynamics, 2016Co-Authors: Choon Ki Ahn, Peng ShiAbstract:In this paper, we investigate the strict dissipativity and asymptotic stability of digital filters in the direct form with Saturation Nonlinearity. First, a novel criterion is presented for the (Q, S, R)-\(\alpha \)-dissipativity of single digital filters in the direct form. By selecting the weighting scalar parameters, this condition reduces to the conditions for \(H_{\infty }\), passivity, and mixed \(H_{\infty }\)/passivity performances. Based on this result, a new sufficient criterion is proposed for the (Q, S, R)-\(\alpha \)-dissipativity of interconnected digital filters in the direct form. A condition for the asymptotic stability of interconnected direct-form digital filters is also presented, and three numerical examples are included to show the effectiveness of the developed theoretical results.
Okyay Kaynak - One of the best experts on this subject based on the ideXlab platform.
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event triggered fuzzy adaptive leader following tracking control of non affine multi agent systems with finite time output constraint and input Saturation
IEEE Transactions on Fuzzy Systems, 2021Co-Authors: Yasaman Salmanpour, Mohammad Mehdi Arefi, Alireza Khayatian, Okyay KaynakAbstract:This paper considers the problem of distributed adaptive fuzzy event-based finite-time prescribed performance leader-following tracking control for heterogeneous nonlinear multi-agent systems (NMASs) over a directed topology. Each agent is considered in a non-affine nonstrict-feedback form under input Saturation and output constraint which contains unknown dynamics and external disturbances. Fuzzy logic systems (FLSs) are exploited as an effective online approximation tool to tackle system uncertainties. By employing the unique property of FLS, the algebraic loop problem is overcome and by designing novel adaptive laws of the FLS weights, the computation burden is decreased significantly. The threshold of the event-triggered condition is improved compared to the conventional relative-threshold mechanism. A modified performance function called finite-time performance function is introduced to constrain the synchronization errors within the prescribed performance bounds in finite time. The dynamic surface control technique is then developed to avoid the issue of the explosion of complexity. Moreover, by developing a new decomposition for the controller gain function resulting from the mean-value theorem and introducing an auxiliary system, the input Saturation Nonlinearity that affects the non-affine form stability is handled. Through the Lyapunov stability analyses, it is shown that the developed control algorithm ensures the closed-loop NMAS trajectory to be cooperatively semi-globally uniformly ultimately bounded. Additionally, the tracking errors are driven to a predefined region around zero in finite time. Finally, the efficiency of the established theoretical results is validated by the simulation studies.
Najib Essounbouli - One of the best experts on this subject based on the ideXlab platform.
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neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input Saturation
IEEE CAA Journal of Automatica Sinica, 2019Co-Authors: Nassira Zerari, Mohamed Chemachema, Najib EssounbouliAbstract:In this paper, an adaptive neural networks ( NNs ) tracking controller is proposed for a class of single-input / single-output ( SISO ) non-affine pure-feedback non-linear systems with input Saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter. Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature. In controller design of the proposed approach, a state observer, based on the strictly positive real ( SPR ) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the Saturation Nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input Saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.
Rastko R. Selmic - One of the best experts on this subject based on the ideXlab platform.
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Neural network control of a class of nonlinear systems with actuator Saturation
IEEE transactions on neural networks, 2006Co-Authors: Wenzhi Gao, Rastko R. SelmicAbstract:A neural net (NN)-based actuator Saturation compensation scheme for the nonlinear systems in Brunovsky canonical form is presented. The scheme that leads to stability, command following, and disturbance rejection is rigorously proved and verified using a general "pendulum type" and a robot manipulator dynamical systems. Online weights tuning law, the overall closed-loop system performance, and the boundedness of the NN weights are derived and guaranteed based on Lyapunov approach. The actuator Saturation is assumed to be unknown and the Saturation compensator is inserted into a feedforward path. Simulation results indicate that the proposed scheme can effectively compensate for the Saturation Nonlinearity in the presence of system uncertainty.
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adaptive neural network output feedback control of nonlinear systems with actuator Saturation
Conference on Decision and Control, 2005Co-Authors: Wenzhi Gao, Rastko R. SelmicAbstract:An indirect adaptive neural network (NN) Saturation compensator is presented for a class of nonlinear systems. Output feedback control is considered where only the system output is assumed to be measurable. The imposed actuator Saturation is assumed to be unknown and treated as the system input disturbance. A NN-based state observer estimates derivatives of the output and another NN-based feedback controller is inserted into a feedforward path to capture the nonlinearities of the observed system and to cancel the effects of the unknown disturbances and the unknown Saturation Nonlinearity. The unknown system states identified by the NN observer are inputs of the NN-based controller. Two NNs interact together to achieve the desired performance. Both adaptive, neural network control laws and on line neural net weights tuning rules are rigorously derived based on feedback linearization and Lyapunov approach. The overall robust adaptive scheme guarantees that the states estimation errors, NN weights estimation errors, and output tracking errors are uniformly ultimately bounded. The simulation conducted indicates the proposed scheme can effectively estimate the unknown nonlinear system states and accommodate the unknown actuator constraints.
Ziyang Zhen - One of the best experts on this subject based on the ideXlab platform.
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Adaptive neural network finite time control for quadrotor UAV with unknown input Saturation
Nonlinear Dynamics, 2019Co-Authors: Qingzheng Xu, Zhisheng Wang, Ziyang ZhenAbstract:This study presents a novel adaptive robust control strategy for the position and attitude tracking of quadrotor unmanned aerial vehicles (UAVs) in the presence of input Saturation, unmodeled nonlinear dynamics, and external disturbances. To deal with the negative effects of completely unknown input Saturation constraints, a nonsymmetric Saturation Nonlinearity approxiator constructed by the hyperbolic tangent function attached with a parameter adjustment mechanism is incorporated into the controller design. Then, a novel neural network (NN) finite time backstepping-based anti-Saturation control approach is proposed by introducing the NN finite time backstepping, designing the new virtual control signals and the modified error compensation mechanism. The proposed approach not only holds the advantages of the NN finite time backstepping control, but also prevents the system from degradation or even instability caused by unknown nonsymmetric Saturation nonlinearities in actuator. The finite time convergence of all signals in the closed-loop aircraft system is guaranteed via Lyapunov finite time methodology despite the input Saturations, unmodeled dynamics, and external disturbances. Finally, numerical simulations are carried out to illustrate the effectiveness and robustness of the proposed controller.
