RBFNN-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics

Chen, Ziyang; Yang, Xiaohui; Liu, Peter

doi:10.1016/j.neucom.2019.06.083

Cited by 65 publications

(43 citation statements)

References 30 publications

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“…In order to verify the effectiveness and robustness of the proposed method under different reference signals, the RBFNN adaptive control method [5] with c 1 = −0.2, β = 3, γ = −0.005, G = 50000, and ε f = −2; the NFTSM control method [29] with K 1 = 1, K 2 = 1, γ 1 = 1.2, γ 2 = 0.8, α 1 = 0.5, and α 2 = 5, and = 0.02; and the sliding mode adaptive robust (SMAR) H ∞ control method [13] with c = 5.5, k s = 0.8, and η = 0.8 are selected to be compared with the proposed method. The speed tracking effect of various control methods when the reference signal is a sine wave is shown in Figure 4.…”

Section: Compared With Other Control Methodsmentioning

confidence: 99%

“…In order to ensure that the x(k), v(k) converges to the equilibrium point within finite-time and avoid the singular problem, the FTSM surface [29] is designed as:…”

Section: Control Law Designmentioning

confidence: 99%

“…In order to realize the fast tracking of a desired speed in a finite-time, in terms of the FTSM controller design procedure in approach [29], the control low with the estimates of RBFNN can be designed as:…”

Section: Control Law Designmentioning

confidence: 99%

“…With the aim to settle the singular problem, referring to the approach [29], we design the saturation function to limit the singular item ϑ z (k):…”

Section: Control Law Designmentioning

confidence: 99%

See 3 more Smart Citations

Non-Singular Fast Terminal Sliding Mode Control of High-Speed Train Network System Based on Improved Particle Swarm Optimization Algorithm

Kong

Zhang

2020

Symmetry

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This paper proposes a non-singular fast terminal sliding mode control strategy based on the self-organizing radial basis function neural network (RBFNN) approximation for the train key network system to realize the safe and reliable operation of the train. In order to improve the RBFNN approximation performance and speed, an improved multi-strategy particle swarm optimization (IMPSO) algorithm, which utilizes multi-strategy evolution ways with a nonlinear deceasing inertia weight to improve the global optimizing performance of particle swarm, is proposed to optimize the structure and parameters for better mapping the highly nonlinear characteristics of train traction braking. In addition, the IMPSO is also introduced into a non-singular fast terminal sliding mode (NFTSM) controller to obtain the most appropriate tuning parameters of the controller and suppresses the chattering phenomenon from sliding mode controller. The stability characteristic of the system under the proposed NFTSM controller is studied based on the Lyapunov theory. Further combined with effective delay prediction and delay compensation methods, the NFTSM high-precision control of the train key nonlinear network system is implemented. The simulation results show that the proposed method has more efficient and robust tracking performance and real-time performance compared with other control methods, which can provide effective means for realizing the symmetrical bus control by automatic train operation (ATO) at both ends of the train, with the safe operation of the train under every complex motion condition.

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Section: Compared With Other Control Methodsmentioning

confidence: 99%

“…In order to ensure that the x(k), v(k) converges to the equilibrium point within finite-time and avoid the singular problem, the FTSM surface [29] is designed as:…”

Section: Control Law Designmentioning

confidence: 99%

Section: Control Law Designmentioning

confidence: 99%

“…With the aim to settle the singular problem, referring to the approach [29], we design the saturation function to limit the singular item ϑ z (k):…”

Section: Control Law Designmentioning

confidence: 99%

See 2 more Smart Citations

Non-Singular Fast Terminal Sliding Mode Control of High-Speed Train Network System Based on Improved Particle Swarm Optimization Algorithm

Kong

Zhang

2020

Symmetry

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show abstract

“…Featured with good non‐linear mapping, online compensation ability and superior approximation characteristics, RBF neural network is widely used to design adaptive strategies [16, 37]. In this paper, a saturated RBF neural network approximator is designed to approximate the uncertain non‐linearity

T_{unc}

, where the outputs of the neural network are kept bounded to make

u_{s}

bounded.…”

Section: Controller Designmentioning

confidence: 99%

Bounded decoupling control for flexible‐joint robot manipulators with state estimation

Cheng

Liu

2020

IET Control Theory & Appl

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Adaptive hierarchical sliding mode controller for tower cranes based on finite time disturbance observer

Zhou

Hong

et al. 2022

Adaptive Control & Signal

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Summary A finite time disturbance observer (FTDO) based adaptive hierarchical sliding mode control (AHSMC) is proposed for 4‐DOF tower crane systems with unknown external disturbances. More specifically, to overcome the unknown disturbances, an FTDO combined with the designed adaptive observation error is presented to estimate both matched and unmatched disturbances. Then a new error dynamics taking into account the unmatched disturbances is defined, based on which, a novel nonsingular fast terminal sliding mode (FTSM) surfaces is designed to improve the convergence of system states. Finally, in order to ensure the stability of each subsystem, an AHSMC law incorporating the suggested FTSM‐type reaching law is presented to realize the anti‐sway and positioning control of tower crane system. The finite‐time stability analysis of the closed‐loop system is derived by Lyapunov technique, and simulation results verify the effectiveness of the presented control strategy.

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RBFNN-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics

Cited by 65 publications

References 30 publications

Non-Singular Fast Terminal Sliding Mode Control of High-Speed Train Network System Based on Improved Particle Swarm Optimization Algorithm

Non-Singular Fast Terminal Sliding Mode Control of High-Speed Train Network System Based on Improved Particle Swarm Optimization Algorithm

Bounded decoupling control for flexible‐joint robot manipulators with state estimation

Adaptive hierarchical sliding mode controller for tower cranes based on finite time disturbance observer

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