Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints
By Kai Zhao, Yongduan Song, Tiedong Ma, Liu He. IEEE Transactions on Neural Networks and Learning Systems, 2018
300 citations recorded by the source corpus.
This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive tracking control scheme is developed, resulting in a solution with several salient features: 1) the control action is continuous and <inline-formula> <tex-math notation="LaTeX">$\mathscr C^{1}$ </tex-math></inline-formula> smooth; 2) the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a controllable rate of convergence that can be uniformly prespecified; 3) with Nussbaum gain in the loop, the tracking error further shrinks to zero as <inline-formula> <tex-math notation="LaTeX">$t\to \infty $ </tex-math></inline-formula>; and 4) the neural network (NN) unit can be safely included in the loop during the entire system operational envelope without the danger of violating the compact set precondition imposed on the NN training inputs. Furthermore, by using the Lyapunov analysis, it is proven that all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness and benefits of the proposed control method are validated via computer simulation.
Subfields: Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/11e5858e63f3ccef7d64708afcea5504e9120c4f
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In Computational Theory and Mathematics
In Control and Systems Engineering
Citations in the corpus
- Observer-Based Fuzzy Adaptive Event-Triggered Control for Pure-Feedback Nonlinear Systems With Prescribed Performance (2019, background): “Specially, by converting the tracking error into a new error variable, the state feedback control issue for SISO nonlinear systems with error constraint in the theoretical framework of Lyapunov function [27], [28] is investigated in [24]–[26].”
- Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization (2020, background): “Based on the expression of ¯ ς( t ) in (7), one has Then, the theorem in [49] lists the properties with respect to the speed function β( t ) .”
- Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems (2019, methodology): “The BLF-based control methods [9], [13], [28], [31], [32], [40], [41] for nonlinear systems normally convert the state constraints into the new bounds on tracking errors, leading to more restrictive conditions on the system initial values.”