Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

Rahimi, Hamed; Howard, Ian; Cui, Lei

doi:10.1016/j.ymssp.2018.03.042

Cited by 52 publications

(31 citation statements)

References 83 publications

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“…where b 1 and b 2 are positive constants, then the solution of the closed-loop system is UUB for bounded initial conditions. Lemma 3 [34]. For variable, ψ, in ψ < 1, tan πψ 2 /2 < πψ 2 sec 2 πψ 2 /2 holds true.…”

Section: Technical Preliminariesmentioning

confidence: 94%

“…Lemma 4 [34]. For any variable, Ψ, and any positive constant, γ, 0 < |Ψ| − Ψtanh(Ψ/γ) < Kγ holds true, where K satisfies K = exp(−(K + 1)), accordingly, K = 0.2785.…”

Section: Technical Preliminariesmentioning

confidence: 99%

“…where, γ 1 is a positive design parameter. The substitution of Equation (34) into Equation (33) yields:…”

Section: Controller Design Proceduresmentioning

confidence: 99%

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Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

et al. 2019

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This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

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Section: Technical Preliminariesmentioning

confidence: 94%

“…Lemma 4 [34]. For any variable, Ψ, and any positive constant, γ, 0 < |Ψ| − Ψtanh(Ψ/γ) < Kγ holds true, where K satisfies K = exp(−(K + 1)), accordingly, K = 0.2785.…”

Section: Technical Preliminariesmentioning

confidence: 99%

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Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

et al. 2019

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“…Lately, barrier Lyapunov function (BLF)-based non-linear adaptive control designs are extensively proposed in the literature to handle output and state constraints for different classes of nonlinear systems [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In these control designs, first, the imposed constraints are converted into equivalent error constraints.…”

Section: Introductionmentioning

confidence: 99%

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

Sachan¹,

Padhi²

2020

IET Control Theory & Applications

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“…Recently, many researchers tried to introduce the intelligent control methods into the force control to improve the tracking performance and robustness of the robotic system [15][16][17][18][19][20]. In [21], the neural network control technique was applied in impedance controller to compensate the uncertainties in an online manner.…”

Section: Introductionmentioning

confidence: 99%

Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Peng

Yang

2019

Complexity

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In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

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Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

Cited by 52 publications

References 83 publications

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

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