The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Ismail Boumhidi - One of the best experts on this subject based on the ideXlab platform.
-
direct adaptive fuzzy sliding mode controller without Reaching Phase for an uncertain three tank system
International Journal of Modelling Identification and Control, 2016Co-Authors: El Mehdi Mellouli, Ismail BoumhidiAbstract:In this paper, a direct adaptive fuzzy sliding mode is proposed to design a new robust controller without Reaching Phase and chattering problems for a multiple-input multiple-output (MIMO) three-tank-system with unknown dynamics and external disturbances. The approach is based on modifying the sliding domain equation through the use of the Mamdani fuzzy logic approaches. The adaptive fuzzy law Takagi-Sugeno (TS) model is used to directly approximate the vector control of the system. Moreover the auxiliary sliding mode control term is incorporated in the control law to attenuate the fuzzy approximation errors and the external disturbances. The stability and robustness of the proposed control scheme are provided. Simulation results are presented which demonstrate the efficiency and robustness of the proposed control scheme.
-
integral sliding mode control without Reaching Phase for a variable speed wind turbine
International Conference on Electrical and Information Technologies, 2016Co-Authors: Belamfedel K Alaoui, Elmahjoub Boufounas, Ismail BoumhidiAbstract:In this paper, sliding mode controller (SMC) and integral sliding mode controller (ISMC) are designed for a variable speed wind turbine. The main objective of the controller is to optimize the energy captured from the wind at below rated power. To overcome the steady state error, the ISMC is introduced. However, in the presence of large uncertainties, ISMC control approach produces oscillatory phenomenon due to the higher needed gain. To eliminate the tradeoffs between the ISMC tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of the exponential function in order to eliminate the Reaching Phase. The stability of the proposed ISMC is analyzed by Lyapunov theory and the control action didn't show any chattering. The simulation results of ISMC are presented and the control performance is compared with conventional SMC.
-
optimal neural network sliding mode control without Reaching Phase using genetic algorithm for a wind turbine
International Conference on Intelligent Systems: Theories and Applications, 2015Co-Authors: Youssef Berrada, Elmahjoub Boufounas, Ismail BoumhidiAbstract:In this paper, an optimal neural network sliding mode control without Reaching Phase based on genetic algorithm (NNSMC) is designed for a variable speed wind turbine. Classical sliding mode control can be used for nonlinear systems. However, it presents some drawbacks linked of chattering, due to the higher needed switching gain in the case of large uncertainties. In order to reduce this gain, neural network is used for the prediction of model unknown parts and hence enable a lower switching gain to be used. Genetic algorithm is used to optimize both, the learning rate of BP and the variable switching gain. The elimination of Reaching Phase yields in a considerable amelioration of system robustness, so the proposed approach is based on the modification of the output tracking error. The performance of the proposed approach is investigated in simulations.
-
optimal h control without Reaching Phase for a variable speed wind turbine based on fuzzy neural network and apso algorithm
International Journal of Modelling Identification and Control, 2015Co-Authors: Elmahjoub Boufounas, Jaouad Boumhidi, Ismail BoumhidiAbstract:This paper presents a novel optimal H∞ tracking-based adaptive fuzzy neural network controller (HAFNNC) for a variable speed wind turbine. The main objective of the controller is to optimise the energy captured from the wind. In the presence of large uncertainties, H∞ control approach produces oscillatory phenomenon due to the higher needed gain. In order to reduce this gain, fuzzy neural network (FNN) with online adaptation of the parameters is used to estimate the uncertain parts of the system plant and hence enable a lower gain to be used. To eliminate the trade-offs between the H∞ tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of both the exponential function and the adaptive particle swarm optimisation (APSO) algorithm. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of the proposed approach.
-
optimal h control without Reaching Phase with the differential evolution pid based on pss for multi machine power system
2015 Intelligent Systems and Computer Vision (ISCV), 2015Co-Authors: Faiza Dib, Ismail BoumhidiAbstract:The objective of this paper is to design a nonlinear robust controller for the multi-machine power systems. We present in this study an optimal H ∞ tracking control without Reaching Phase combined with the Proportional Integral Derivative based on Power System Stabilizer (PID-PSS) optimized by Differential Evolution algorithm. To eliminate the tradeoffs between the H» tracking performance and the high gain at the control input, we have defined a new method based on the modified output tracking error by using the exponential function. The Differential Evolution algorithm is used in this study to find the optimal values of the three parameters (Kp, Ki, Kd) of (PID-PSS) and also used to tune the exponential function of the tracking error. The proposed approach is designed to eliminate completely the Reaching Phase and to enhance the stability and the dynamic response of the multi-machine power system. In order to test the effectiveness of the proposed method, the simulation results show the damping of the oscillations of the angle and angular speed with reduced overshoots and quick settling time.
Yingmin Jia - One of the best experts on this subject based on the ideXlab platform.
-
fixed time consensus tracking control for second order multi agent systems with bounded input uncertainties via nfftsm
Iet Control Theory and Applications, 2017Co-Authors: Yi Huang, Yingmin JiaAbstract:This paper is devoted to the fixed-time consensus tracking control for second-order multi-agent systems with bounded input uncertainties under a weighted directed topology. Firstly, a novel non-singular fixed-time fast terminal sliding mode (NFFTSM) surface with bounded convergence time in regardless of the initial states is designed, and the explicit expression of the settling time is provided. Fair and unprejudiced comparisons show that the proposed NFFTSM has faster convergence performance than most typical terminal sliding modes in the existed results. Subsequently, by employing the proposed NFFTSM, a non-singular fixed-time distributed control protocol for second-order multi-agent systems is designed, which only requires one-hop information of the neighbours without the global topology information and has the advantage of fast convergence performance both in the Reaching Phase and sliding Phase. Rigorous proofs show that the fixed-time consensus tracking control for second-order multi-agent systems can be guaranteed by the proposed distributed control protocol. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed control scheme.
-
finite time attitude tracking control for a rigid spacecraft using time varying terminal sliding mode techniques
International Journal of Control, 2015Co-Authors: Lin Zhao, Yingmin JiaAbstract:This paper investigates the finite-time attitude tracking control for a rigid spacecraft in the presence of inertia uncertainties and external disturbances. Two novel time-varying terminal sliding mode control algorithms are derived for attitude tracking control system. The proposed two control algorithms not only eliminate the Reaching Phase of the conventional sliding mode control but also guarantee the tracking errors converge to zero in finite time. Moreover, the singularity problem can be avoided. Simulation results are provided to demonstrate the effectiveness of the proposed design methods.
An Min Zou - One of the best experts on this subject based on the ideXlab platform.
-
distributed consensus control for multi agent systems using terminal sliding mode and chebyshev neural networks
International Journal of Robust and Nonlinear Control, 2013Co-Authors: An Min Zou, Krishna Dev Kumar, Zeng-guang HouAbstract:This paper investigates the problem of consensus tracking control for second-order multi-agent systems in the presence of uncertain dynamics and bounded external disturbances. The communication ?ow among neighbor agents is described by an undirected connected graph. A fast terminal sliding manifold based on lumped state errors that include absolute and relative state errors is proposed, and then a distributed finite-time consensus tracking controller is developed by using terminal sliding mode and Chebyshev neural networks. In the proposed control scheme, Chebyshev neural networks are used as universal approximators to learn unknown nonlinear functions in the agent dynamics online, and a robust control term using the hyperbolic tangent function is applied to counteract neural-network approximation errors and external disturbances, which makes the proposed controller be continuous and hence chattering-free. Meanwhile, a smooth projection algorithm is employed to guarantee that estimated parameters remain within some known bounded sets. Furthermore, the proposed control scheme for each agent only employs the information of its neighbor agents and guarantees a group of agents to track a time-varying reference trajectory even when the reference signals are available to only a subset of the group members. Most importantly, finite-time stability in both the Reaching Phase and the sliding Phase is guaranteed by a Lyapunov-based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controller and show that the proposed controller exceeds to a linear hyperplane-based sliding mode controller. Copyright (C) 2011 John Wiley & Sons, Ltd.
-
Finite-time attitude tracking control for spacecraft using terminal sliding mode and chebyshev neural network
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2011Co-Authors: An Min Zou, Zeng-guang Hou, Krishna Dev Kumar, Xi LiuAbstract:A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the Reaching Phase and the sliding Phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.
Xi Liu - One of the best experts on this subject based on the ideXlab platform.
-
Finite-time attitude tracking control for spacecraft using terminal sliding mode and chebyshev neural network
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2011Co-Authors: An Min Zou, Zeng-guang Hou, Krishna Dev Kumar, Xi LiuAbstract:A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the Reaching Phase and the sliding Phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.
Zeng-guang Hou - One of the best experts on this subject based on the ideXlab platform.
-
distributed consensus control for multi agent systems using terminal sliding mode and chebyshev neural networks
International Journal of Robust and Nonlinear Control, 2013Co-Authors: An Min Zou, Krishna Dev Kumar, Zeng-guang HouAbstract:This paper investigates the problem of consensus tracking control for second-order multi-agent systems in the presence of uncertain dynamics and bounded external disturbances. The communication ?ow among neighbor agents is described by an undirected connected graph. A fast terminal sliding manifold based on lumped state errors that include absolute and relative state errors is proposed, and then a distributed finite-time consensus tracking controller is developed by using terminal sliding mode and Chebyshev neural networks. In the proposed control scheme, Chebyshev neural networks are used as universal approximators to learn unknown nonlinear functions in the agent dynamics online, and a robust control term using the hyperbolic tangent function is applied to counteract neural-network approximation errors and external disturbances, which makes the proposed controller be continuous and hence chattering-free. Meanwhile, a smooth projection algorithm is employed to guarantee that estimated parameters remain within some known bounded sets. Furthermore, the proposed control scheme for each agent only employs the information of its neighbor agents and guarantees a group of agents to track a time-varying reference trajectory even when the reference signals are available to only a subset of the group members. Most importantly, finite-time stability in both the Reaching Phase and the sliding Phase is guaranteed by a Lyapunov-based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controller and show that the proposed controller exceeds to a linear hyperplane-based sliding mode controller. Copyright (C) 2011 John Wiley & Sons, Ltd.
-
Finite-time attitude tracking control for spacecraft using terminal sliding mode and chebyshev neural network
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2011Co-Authors: An Min Zou, Zeng-guang Hou, Krishna Dev Kumar, Xi LiuAbstract:A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the Reaching Phase and the sliding Phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.
