Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics

By Liangyong Wang, Tianyou Chai, Lianfei Zhai. IEEE transactions on industrial electronics (1982. Print), 2009

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Fixed-Time Attitude Control for Rigid Spacecraft With Actuator Saturation and Faults (2016, methodology): “Remark 2: The switching method (5) is inspired by [19] and [20] to avoid the singularity.”
Sliding-Mode Robot Control With Exponential Reaching Law (2011, methodology): “Finally, a neural-network sliding-mode approach is proposed in [ 13 ] to control a robot manipulator.”
Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network (2011, unknown): “Motivated by the work of [23], we propose a modified fast TSM manifold as”
Terminal Sliding Mode Control – An Overview (2021, background): “In [88], a neural network-based TSMC was de-”
Adaptive attitude tracking control for rigid spacecraft with finite-time convergence (2013, unknown): “A modified TSMS (Wang et al., 2009) s = ẋ + k1β(x) is proposed with β(x) as β(x) = x r1 r2 , if s̄ = 0 or s̄ ≠ 0, |x| ≥ ε ι1x + ι2 sgn(x)x2, if s̄ ≠ 0, |x| < ε (1) with ι1 = (2 − r1 r2 )ε r1 r2 −1, ι2 = ( r1 r2 − 1)ε r1 r2 −2, s̄ = ẋ + k1x r1 r2 and sgn(·) denotes the signum function. ε > 0 is…”
Partial Integrated Missile Guidance and Control with Finite Time Convergence (2013, methodology): “The singularity was avoided in [13] bymaking a smooth modification to the commonly used terminal sliding surface.”