Barrier Lyapunov Functions for the control of output-constrained nonlinear systems
By Keng Peng Tee, Shuzhi Sam Ge, Francis Eng Hock Tay. at - Automatisierungstechnik, 2009
2,743 citations recorded by the source corpus.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/16d657b3a360fc152eefdf3d550f70748adec388
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In Control and Systems Engineering
Citations in the corpus
- Stabilization with guaranteed safety using Control Lyapunov-Barrier Function (2016, methodology): “One important characteristic of the potential function in such method is that it grows unbounded as it reaches the boundary of the obstacle (or the set of unsafe state), akin to the works in Ngo et al. (2005) and Tee et al. (2009) which is generally complicated and difficult to construct.”
- Modeling and Vibration Control for a Nonlinear Moving String With Output Constraint (2015, methodology): “Barrier Lyapunov functions have been used to cope with constrained problem for ODE systems [36], [37].”
- Control Barrier Functions for Signal Temporal Logic Tasks (2019, background): “Barrier functions guarantee forward invariance of a set with respect to the system dynamics of an unforced system [11].”
- Adaptive Control of a Flexible Crane System With the Boundary Output Constraint (2014, methodology): “The barrier Lyapunov function is a novel concept used for dealing with the control problems with constraints [26], [27].”
- Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, background): “…Property 1, (22) is simplified as Substituting (18) into (23) and letting D (τ) = τ − (cid:12)τ , (cid:12)τ is the error, results in We design the model-based control as .. . where ˙ α is given as [64] substituting (25) into (24) results in where the gains k i > 0 and K 2 = K T 2 > 0 n × n .”
- Control of nonlinear systems with partial state constraints using a barrier Lyapunov function (2011, methodology): “In this article, we extend the work of Tee et al. (2009b), on output-constrained systems, to a more difficult problem wherein constraints are present in some or even all of the states.”
- Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, methodology): “In [50], the BLF is employed for SISO nonlinear systems to prevent constraint violation.”
- Control Barrier Functions for Systems with High Relative Degree (2019, background): “Barrier functions (BF) are Lyapunov-like functions [16][17], whose use can be traced back to optimization problems [5].”
- Control of nonlinear systems with time-varying output constraints (2009, methodology): “The BLFbased design framework accommodates adaptive control design for handling not only parametric uncertainty (Tee et al., 2009b), ✩ This paper was not presented at any IFAC meeting.”
- Control Barrier Functions: Theory and Applications (2019, background): “Yet, one must be careful about directly generalizing Lyapunov (as done, in particular, in [38]).”
- Reference and command governors for systems with constraints: A survey on theory and applications (2017, background): “The treatment of unmeasured disturbances and parametric uncertainties is also detailed.”
- Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, positive): “A BLF is introduced to ensure that the full-state constraints are not violated and the NN units are able to take their action from the very beginning and play their learning/approximating role safely during the entire system operational envelope.”