Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

By Wei He, Yiting Dong, Changyin Sun. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016

761 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/126b6bf0f3fd8c5884295898cf3d34e25ac44eb7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Wei He
2. Shaocheng Tong
3. Changyin Sun
4. Yanjun Liu
5. Chenguang Yang
6. Zhao Yin
7. Yiting Dong
8. Chun Lung Philip Chen

In Control and Systems Engineering

1. Wei He
2. Shaocheng Tong
3. Yanjun Liu
4. Changyin Sun
5. Yiting Dong
6. Chun Lung Philip Chen
7. Chenguang Yang
8. Shuzhi Sam Ge

Citations in the corpus

• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, background): “Lemma 4 [79], [80]: Consider the basis function of Gaussian RBF NN with Z being the input vector, if Ẑ = Z − ςψ̄ where ψ̄ is a bounded vector and constant ς > 0, then we have”
• Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning (2017, neutral): “It should be noted that there already exists a number of methods to address input nonlinearity problems such as input dead-zone and saturation [47]–[52].”
• Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay (2019, methodology): “By using NNs or FLSs, the adaptive controllers were presented for singleinput-single-output (SISO) nonlinear systems in [7]–[16], nonlinear multi-input-multioutput (MIMO) systems in [17]–[19] and discrete-time systems in [20] and [21] to approximate unknown functions.”
• Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems (2018, neutral): “,n − 1 (9) żn = −knzn + θ̃nφn(xn) (10)”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, background): “Bulk of existing papers consider the nonlinear systems without constraints [53]–[58], nonlinear systems with input constraints [59]–[63], nonlinear systems with constant output constraints [64], [65], or certain SISO nonlinear systems subjected to time-varying output constraints [52].”
• Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, methodology): “In [48], adaptive impedance control is presented for an n-link robotic manipulator with input saturation.”
• Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision (2017, methodology): “In order to well approximate the robot dynamics and considering both the accuracy and the computational efficiency, we divide the inputs of RBFNN into two groups, with one group containing [qTi , α̇ T i ] T ∈ R6 and another [qTi , q̇Ti , αTi ]T ∈ R9 , and employ three centers for each input dimensio”
• Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, neutral): “For nonlinear systems with unknown functions and input nonlinearities, several adaptive fuzzy or neural control methods were given in [19]–[21], [63], [64], [67], [68], and [76].”
• Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals (2017, background): “Motivated by the previous works [7], [18], impedance control has been extensively investigated, which adjusts the mechanical impedance specified by a target model.”
• Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone (2017, methodology): “An adaptive NN control is proposed in [25] for”