Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone

Xu, Bin; Sun, Fuchun; Pan, Yongping; Chen, Badong

doi:10.1109/tsmc.2016.2562502

Cited by 169 publications

(83 citation statements)

References 67 publications

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“…Nevertheless, the nonsmooth dynamics, including nonlinear friction [7][8][9] and dead-zone [10][11][12], introduced by transmission devices may deteriorate the control performance. To reduce the effect of the nonlinear friction, various control algorithms have been proposed such as sliding mode technique (SMC) [13][14][15][16][17][18] and adaptive control [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Wang

Gao

et al. 2018

Complexity

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This paper proposes an adaptive barrier controller for servomechanisms with friction compensation. A modified LuGre model is used to capture friction dynamics of servomechanisms. This model is incorporated into an augmented neural network (NN) to account for the unknown nonlinearities. Moreover, a barrier Lyapunov function (BLF) is utilized to each step in a backstepping design procedure. Then, a novel adaptive control method is well suggested to ensure that the full-state constraints are within the given boundary. The stability of the closed-loop control system is proved using Lyapunov stability theory. Comparative experiments on a turntable servomechanism confirm the effectiveness of the devised control method.

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Section: Introductionmentioning

confidence: 99%

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Wang

Gao

et al. 2018

Complexity

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“…Input saturation has an effect on control performance, which has been investigated in the last few decades. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] The Takagi-Sugeno fuzzy modeling approach was utilized to control the nonlinear systems with actuator saturation in the work of Cao and Lin. 37 The stabilization problem was addressed for a class of Hamiltonian systems with state time-delay and input saturation in the work of Sun.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Ren

Shi

2017

Adaptive Control & Signal

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SummaryIn this article, the problems of modeling and controlling are investigated for a suspension cable system of a helicopter with input saturation, system parameter uncertainties, and external disturbances by using the boundary control method.In accordance with the Hamilton's principle, the model of the suspension cable system of a helicopter is established by using a set of partial differential and ordinary differential equations. Considering nonsymmetric saturation constraint, the auxiliary systems are designed to handle with the effect of input saturation. Considering Lyapunov's direct method and the designed auxiliary systems, two robust adaptive boundary controllers are provided by the actuators at the helicopter and the box. Under the proposed controllers, the error between the bottom payload and the target location and the vibration range are uniformly ultimately bounded. Moreover, they will converge to a small neighborhood of zero by selecting the suitable parameters. Meanwhile, to guarantee the validity of the proposed adaptive boundary control laws, some sufficient conditions are raised. Simulation results are provided to verify that the effectiveness of the designed controllers in this paper.

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“…The concept of MPC algorithm is based on the moving horizon approach where the control action is computed to obtain the desired performance over a finite time horizon under uncertainties and constraints. 1 The use of the constraints clearly distinguishes MPC from other control methods such as neural network-based approach, [2][3][4][5][6][7][8] leading to a more reliable controller and tighter control with no requirement on training data. However, the heavy online computational burden 9 and numerical condition of the control algorithm are the main obstacles in the application of MPC.…”

Section: Introductionmentioning

confidence: 99%

Block diagonal dominant remotely operated vehicle model simulation using decentralized model predictive control

Lin

Chin

2017

Advances in Mechanical Engineering

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Model predictive control on a highly coupled open-frame remotely operated vehicle system subjected to uncertain disturbances has always been a challenge. A decentralized model predictive control uses a design scaling to balance the interactions between the loops to achieve a block diagonal dominant remotely operated vehicle model is used. The numerical stability of the model predictive control algorithm improves despite the sensitivity of the control parameters on the output performance. The model predictive control gives a better two-error norm performance and more tuning options to control the velocity and position output as compared to other design scaling methods and other controllers such as proportional-integral-derivative, fuzzy logic, sliding mode, and proportional-integral plus proportional cascaded control when subjected to underwater current disturbance.

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Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone

Cited by 169 publications

References 67 publications

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Block diagonal dominant remotely operated vehicle model simulation using decentralized model predictive control

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