Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities

By Mou Chen, Shuzhi Sam Ge, Bernard Voon Ee How. IEEE Transactions on Neural Networks, 2010

725 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/dd7e999b2db8c1e53efac83d96c2aa3d1cd78c59

How researchers see this paper

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

In Artificial Intelligence

1. Shaocheng Tong
2. Yong-ming Li
3. Tie-shan Li
4. Peng Shi
5. Yanjun Liu
6. Qi Zhou
7. Chun Lung Philip Chen
8. Shuai Sui

In Computer Networks and Communications

1. Shaocheng Tong
2. Yong-ming Li
3. Mou Chen
4. Yanjun Liu
5. Hongyi Li
6. Peng Shi
7. Xu Jin
8. Chun Lung Philip Chen

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Mou Chen
4. Hongyi Li
5. Yanjun Liu
6. Shuzhi Sam Ge
7. Peng Shi
8. Wei He

Citations in the corpus

• 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].”
• Finite-Time Adaptive Fuzzy Output Feedback Dynamic Surface Control for MIMO Nonstrict Feedback Systems (2019, methodology): “[9], [10] proposed the fuzzy/NNs adaptive control methods for multi-input and multi-output (MIMO) strict feedback nonlinear systems.”
• Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation (2015, background): “Robust adaptive neural network control was proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with input nonlinearities [6].”
• Fault tolerant finite-time leader-follower formation control for autonomous surface vessels with LOS range and angle constraints (2016, methodology): “More discussion regarding the auxiliary system in similar designs can be found in Chen et al. (2010, 2011) and Jin (2016). The adaptive estimator θ̂1 as in (15) is designed as”
• Adaptive Fuzzy Control of Nonlinear Systems With Unmodeled Dynamics and Input Saturation Using Small-Gain Approach (2017, background): “[51] investigated the problem of tracking control for a category of MIMO systems with input saturation.”
• Composite Adaptive Fuzzy Output Feedback Control Design for Uncertain Nonlinear Strict-Feedback Systems With Input Saturation (2015, background): “[28] extended the results in [25] and [26] to the MIMO and interconnected large-scale uncertain nonlinear systems with input saturation.”
• Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer (2013, background): “Robust adaptive NN control was proposed for a class of uncertain MIMO nonlinear systems with input nonlinearities [65].”
• Adaptive Fuzzy Control via Observer Design for Uncertain Nonlinear Systems With Unmodeled Dynamics (2013, background): “The motivation behind this study is to develop adaptive control for nonlinear systems which do not satisfy the matching condition based on the backstepping design [23], [24], [28].”
• Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, background): “In [46], a robust adaptive neural control approach is proposed for a class of multi-input and multi-output nonlinear systems with input nonlinearities.”
• Adaptive Postcapture Backstepping Control for Tumbling Tethered Space Robot–Target Combination (2016, background): “In [15], command filters are presented to implement physical constraints on the virtual control laws, and the tedious analytic computations of time derivatives of virtual control laws are canceled.”
• Fuzzy Adaptive Output Feedback Control of MIMO Nonlinear Systems With Partial Tracking Errors Constrained (2015, background): “The proposed adaptive controllers in [12]–[16] discarded the restrictive assumption in [1]–[11] that the states are available for the controller design.”
• Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems (2015, background): “However, the drawback of explosion of complexity caused by the repeated differentiations of the virtual input [4], [5] for the adaptive backstepping is not resolved by approximation-based adaptive NN or fuzzy backstepping control [28].”
• 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.”
• Observer-Based Adaptive Fuzzy Tracking Control of MIMO Stochastic Nonlinear Systems With Unknown Control Directions and Unknown Dead Zones (2015, background): “In [17], [18], [23], and [24], the authors addressed the same problem as this paper; the controlled plants were restricted to MIMO deterministic nonlinear systems.”
• Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation (2016, methodology): “For example, considering the effect of the input saturation, [27] proposed a robust adaptive NN control approach for a class of uncertain nonlinear MIMO systems, while [28] investigated the fuzzy adaptive control approach for a class”
• Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems. (2013, background): “Robust adaptive neural network control was proposed for a class of uncertain MIMO nonlinear systems with input nonlinearities [41].”
• Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, methodology): “Lemma 2 (Usmani, 1987; Chen et al., 2010).”