Approximation-based control of nonlinear MIMO time-delay systems

By Shuzhi Sam Ge, Keng Peng Tee. at - Automatisierungstechnik, 2007

361 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/5cd01d308ab549515a79b668c718ddf207db32f5

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. Mou Chen
5. Yanjun Liu
6. Chun Lung Philip Chen
7. Peng Shi
8. Shuzhi Sam Ge

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Mou Chen
4. Yanjun Liu
5. Shuzhi Sam Ge
6. Peng Shi
7. Tie-shan Li
8. Chun Lung Philip Chen

Citations in the corpus

• Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, methodology): “In [20], approximation-based control was presented for a class of MIMO nonlinear systems in block-triangular form with unknown state delays.”
• Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, neutral): “…technique for the uncertain MIMO nonlinear systems, and various robust adaptive control strategies have been proposed (Chang, 2000, 2001; Chang & Yen, 2005; Ge, 1998; Ge & Wang, 2004; Ge & Tee, 2007; Ge, Li, Zhang, & Lee, 2004; Ge, Zhang, & Lee, 2004; Lee & Lee, 2004; Zhang, Ge, & Lee, 2005).”
• Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach (2012, neutral): “For instance, see [4], [9], [10], [12], [23], [26], [40], and [51].”
• Tuning functions‐based robust adaptive tracking control of a class of nonlinear systems with time delays (2012, background): “Then, it should be pointed out that, in the aforementioned literature [20, 21, 25], the designed controller depended on time delays or their upper bounds.”
• Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation (2016, methodology): “3) By using new DSC technique, the proposed fuzzy adaptive control approach can overcome the defect of “explosion of complexity” that exists in the references [16]–[20], thus the computational burden of the control algorithm can be reduced greatly.”
• Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique (2011, background): “Lemma 3 [ 48 ]: For 1 ≤ j ≤ n, define the set Ωczj given by Ωczj := {zj||zj| 48 ] is to introduce hyperbolic tangent function tanh(z1/ν1).”
• Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay (2019, methodology): “The authors further presented some significant control methods for different kinds of MIMO nonlinear systems with constant delays in [60] and time-varying delays in [61].”
• Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems (2011, background): “It is noted that, till now, very a few papers study the adaptive control problem on the general nonlinear discrete-time system, and they all focus on some special class of nonlinearities [37].”