Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics

By Hongwei Zhang, Frank L. Lewis. at - Automatisierungstechnik, 2012

1,005 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/fde0e3ed9f30e7bf11e03b0ae4ebfcfb57013398

How researchers see this paper

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

In Computer Networks and Communications

1. Zhongkui Li
2. Frank L. Lewis
3. Z. Duan
4. Wei Ren
5. C. Wen
6. Jun Wang
7. Zongli Lin
8. Zhouhua Peng

In Control and Systems Engineering

1. Shaocheng Tong
2. Peng Shi
3. Yanjun Liu
4. Jun Wang
5. C. Wen
6. Zhouhua Peng
7. Zhongkui Li
8. Dan Wang

Citations in the corpus

• Event-Triggered Adaptive Tracking Control for Multiagent Systems With Unknown Disturbances (2020, neutral): “Lemma 3 [38]: Define e1 = col{e1,1, e2,1, .”
• Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation (2018, background): “Under Assumption 1, H is nonsingular [17], and we obtain ‖z‖ ≤ ‖e‖/o(H ) where o(H ) is the smallest singular value of H .”
• Adaptive Consensus of Multi-Agent Systems With Unknown Identical Control Directions Based on A Novel Nussbaum-Type Function (2014, methodology): “An adaptive neural network control method is adopted in [9]–[11] to design the control input.”
• Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems (2016, background): “Based on the universal approximation property of neural networks (NNs), Zhang and Lewis [18] proposed a cooperative tracking control approach.”
• Consensus of high-order multi-agent systems with large input and communication delays (2014, methodology): “For single input high-order nonlinear multi-agent systems with unknown dynamics, an adaptive cooperative tracking control scheme is considered in Zhang and Lewis (2012).”
• Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties (2013, background): “First, the agents in [25,26] are restricted to be first-order and special high-order systems.”
• Distributed adaptive control for consensus tracking with application to formation control of nonholonomic mobile robots (2014, methodology): “This is because under these two cases, matrices (L+B) and (L + LT + 2B) are symmetric positive definite, respectively (Ren and Cao, 2010; Zhang and Lewis, 2012).”
• Online adaptive optimal control for continuous-time Markov jump linear systems using a novel policy iteration algorithm (2015, neutral): “The extension to the nonlinear field was given by [29][28][15][27][26].”
• Observer‐based leader‐following consensus of uncertain nonlinear multi‐agent systems (2017, background): “To solve the consensus problem and describe the information exchange between agents, basic graph theory and some notations are introduced as in [7].”
• Cooperative Adaptive Fuzzy Tracking Control for Networked Unknown Nonlinear Multiagent Systems With Time-Varying Actuator Faults (2014, background): “1) All nodes in graph G synchronize to the leader node 0 with bounded residual error in finite time, all signals in the closed-loop system as well as the tracking errors δi(t), i = 1, . . . , N are CUUB, namely, for all t ≥ Tm , belong to the adjustable set Ω and Ωδ defined as Ω = { (s(t), θ̃(t), ˜̄”
• Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, neutral): “that for the systems with similar (or identical) structures to EL systems, the issue of full-state constraints is not easy to handle if the filtered error-based control method is utilized [25], [36], [37].”