Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer

By Mou Chen, Shuzhi Sam Ge. IEEE transactions on industrial electronics (1982. Print), 2015

444 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/83bd03d5afe0ce2cc02b2e840fb6eb7566d5a5fb

How researchers see this paper

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

In Control and Systems Engineering

1. Wei He
2. Shuzhi Sam Ge
3. Chenguang Yang
4. Peng Shi
5. Shaocheng Tong
6. Changyin Sun
7. Shi-lu Dai
8. Shude He

Citations in the corpus

• Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer (2017, background): “Accordingly, if we choose appropriate parameters for the proposed disturbance observer, the estimated error will converge to a compact set [46], [47].”
• Network-Based Event-Triggered Control for Singular Systems With Quantizations (2016, background): “In networked control systems (NCSs), the sharing of limited network bandwidth often causes network-induced delays, and data packet dropouts and disorder, which can deteriorate the performance and even destabilize the systems [5]–[11].”
• Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning (2017, methodology): “A radial basis function (RBF) NN was presented to derive the adaptive controller for systems subject to external disturbances and unknown hysteresis in [44].”
• Command Filter-Based Adaptive Fuzzy Finite-Time Output Feedback Control of Nonlinear Electrohydraulic Servo System (2022, methodology): “In [17], the approximation capability of radial basis function neural network is employed to deal with unknown nonlinear function terms in uncertain nonlinear systems.”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, methodology): “The gain matrix K1 and K2 are chosen as K1 = diag[12, 12, 12], K2 = diag[8, 8, 8] which is the same as MB control, and σ1 = σ2 = σ3 = 0.”
• Leader–Follower Formation Control of USVs With Prescribed Performance and Collision Avoidance (2019, unknown): “In what follows, the unknown positive constants τ̄w li , l = 1, 2, 3, and unknown constant vector Θi are solved using model-based adaptive control techniques, and the unknown nonlinear function D(νi)νi is approximated by NNs [42]–[44].”
• Platoon Formation Control With Prescribed Performance Guarantees for USVs (2018, methodology): “The control design is based on PPC methodology [15], DSC technique [26], [27], NN approximation [28], [29], disturbance observers [30]–[34], backstepping procedure, and Lyapunov synthesis.”