Control of nonlinear systems with partial state constraints using a barrier Lyapunov function
By Keng Peng Tee, Shuzhi Sam Ge. International Journal of Control, 2011
466 citations recorded by the source corpus.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/0e6651ebc092f1e4f17a89de70f8020bc1248a9c
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In Control and Systems Engineering
- Shaocheng Tong
- Wei He
- Yanjun Liu
- Yong-ming Li
- Chun Lung Philip Chen
- Kai Zhao
- Yongduan Song
- Tong Wang
Citations in the corpus
- Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay (2019, methodology): “By using backstepping technique and BLF, two adaptive constrained controllers were proposed for partial state constrained systems in [33] and output constrained systems in [34].”
- Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems (2019, methodology): “Remark 2: The novelty of the nonlinear transformation as given in (3) is twofold: 1) it provides an explicit (direct) way to deal with the full-state constraints, preventing the conservative step of converting original state constraints into new bounds on tracking errors [6], [9], [12], [13], [15], ”
- Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, unknown): “Similar to [6] and [53], we can obtain kb1 = 0.”
- Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints (2019, methodology): “Many existing adaptive fuzzy and neural networks based control design methods have been proposed for nonlinear systems with state constraints, by utilizing the Barrier Lyapunov functions (BLFs) [11]–[15].”
- Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints (2021, background): “Assumption 1 [19], [22], [46]: Assume that all the states (except output y ) are immeasurable and constrained in the compact sets, i.e., | x i | < k c i , where k c i is a known positive constant.”
- Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints (2016, background): “Tee and Ge [44] addressed the tracking problem of strict-feedback systems with constraints on the states via an elegant control scheme.”