Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

By Wei He, Yuhao Chen, Zhao Yin. IEEE Transactions on Cybernetics, 2016

1,189 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/3a04c2fd9f0f740fa37c2e9e86414a171f5c22d6

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. Yanjun Liu
4. Changyin Sun
5. Yiting Dong
6. Yongduan Song
7. Kai Zhao
8. Chenguang Yang

In Computer Networks and Communications

1. Shaocheng Tong
2. Wei He
3. Yanjun Liu
4. Changyin Sun
5. Chun Lung Philip Chen
6. Zhao Yin
7. Hongyi Li
8. Jun Wang

In Control and Systems Engineering

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

Citations in the corpus

• Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints (2019, methodology): “Although the existing works in [16]–[18] also studied the full state constraints issue, they cannot be applied to stochastic switched nonlinear systems.”
• Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay (2017, background): “in [37] proposed an adaptive control approach for an uncertain n-link robot system, wherein the full-state constraints were considered.”
• Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks (2018, background): “constraints during motion control [16], [44], [45], [45]–[49].”
• Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer (2017, background): “According to the Lemma 1 cited from [35], we have where and λ min ( • ) and λ max ( • ) are defined as the minimum and maximum eigenvalues of matrix • , respectively.”
• Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay (2019, methodology): “As the main factor of limiting the system performance, constraints often appear in the real industrial process unavoidably, such as nonuniform gantry crane [22], flexible crane systems [23], chemical systems [24], and robotic manipulator [25].”
• Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, neutral): “put and full-state (or partial-state) constraints [8], [26]–[28].”
• Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation (2016, background): “Adaptive NN control is presented to compensate the effect of output constraint for an uncertain robot in [44].”
• Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, neutral): “The tracking control problem was discussed in [55] for a class of uncertain n-link full-state constraint robot.”
• Teleoperation Control Based on Combination of Wave Variable and Neural Networks (2017, methodology): “The radial basis function (RBF) NN is a highly effective method and has been extensively used for control design of uncertain robot systems [35].”
• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, background): “Numerous approaches have been proposed to approximate the control problem of the system with uncertainties [11]–[19].”
• Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, methodology): “To handle output constraints, many techniques have been developed [48]–[50].”