Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
Maximizing regions of attraction via backstepping and CLFs with singularities
Systems & Control Letters
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Output-feedback adaptive control of electrostatic microactuators
ACC'09 Proceedings of the 2009 conference on American Control Conference
Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function
IEEE Transactions on Neural Networks
Brief paper: Control of nonlinear systems with time-varying output constraints
Automatica (Journal of IFAC)
Adversarial Ground Target Tracking Using UAVs with Input Constraints
Journal of Intelligent and Robotic Systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this paper, we present control designs for single-input single-output (SISO) nonlinear systems in strict feedback form with an output constraint. To prevent constraint violation, we employ a Barrier Lyapunov Function, which grows to infinity when its arguments approach some limits. By ensuring boundedness of the Barrier Lyapunov Function in the closed loop, we ensure that those limits are not transgressed. Besides the nominal case where full knowledge of the plant is available, we also tackle scenarios wherein parametric uncertainties are present. Asymptotic tracking is achieved without violation of the constraint, and all closed loop signals remain bounded, under a mild condition on the initial output. Furthermore, we explore the use of an Asymmetric Barrier Lyapunov Function as a generalized approach that relaxes the requirements on the initial conditions. We also compare our control with one that is based on a Quadratic Lyapunov Function, and we show that our control requires less restrictive initial conditions. A numerical example is provided to illustrate the performance of the proposed control.