Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
In this paper, a novel robust adaptive barrier Lyapunov function (BLF)-based backstepping controller has been proposed for a class of interconnected, multi-input-multi-output (MIMO) unknown nonaffine nonlinear systems with asymmetric time-varying (ATV) state constraints. The design involves a neural-network-based online approximator to cope with uncertain dynamics of the system. To tune its weights, a novel adaptive law is proposed based on the Hadamard product. A theorem has also been proposed to have the bounds on virtual control signals beforehand. This theorem eliminates the need for tedious offline computation for the feasibility condition on the virtual controller in BLF-based controller design. To overcome the problem of unknown control gain in the nonaffine system, Nussbaum gain has been used during the design. A simulation study on the robot manipulator in task space has been performed to illustrate the effectiveness of the proposed methodology.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TCYB.2019.2923849 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
This article presents an indirect adaptive interval type-3 (IT3) tracking fuzzy control for a class of unknown nonaffine nonlinear discrete-time networked control systems (NCSs) with denial-of-service (DoS) attacks. To mitigate the adverse effects of attacks, a novel two-mode attack compensator, incorporating an IT3 fuzzy model (IT3FM), is proposed to handle the nonlinear dynamics and uncertainties of NCSs by estimating the unavailable system output during active attacks. Then, an updating algorithm for parameter adjustment, guaranteed to converge via Lyapunov theory, is presented.
View Article and Find Full Text PDFModel-free reinforcement learning control with zero-min barrier functions for constrained systems.
Neural Netw
September 2025
Robotics and Advanced Manufacturing, Center for Research and Advanced Studies (CINVESTAV), 1062 Industria Metalurgica Av., Ramos Arizpe, 25903, Mexico. Electronic address:
The primary focus of this research is to develop an adaptive output feedback controller designed to minimize a cost-to-go function subject to constraints on input, output, and tracking error for a class of unknown non-affine discrete-time systems. The problem formulation addresses a general class of non-affine dynamics, exemplified by a high-gain DC motor torque control system, in which maintaining safe operation within prescribed input and output limits is critical. These physical limitations necessitate the incorporation of constraints to ensure the plant operates within safe regions.
View Article and Find Full Text PDFHierarchical Optimization Design for Autonomous Flight of Vision-Based Quadrotor Using Reinforcement Learning.
IEEE Trans Cybern
July 2025
Although quadrotor has been widely used in practical engineering, its autonomous flight ability needs to be improved in complex operating environment. The autonomous flight problem for quadrotor with monocular vision is investigated, which is divided into control layer and decision layer in this article. Reinforcement learning method is utilized for hierarchical optimization to ensure that quadrotor completes narrow space traversal tasks safely and efficiently.
View Article and Find Full Text PDFPractical prescribed-time output tracking control of uncertain nonaffine systems with input saturation based on time base generator.
ISA Trans
July 2025
Hangzhou International Innovation Institute, Beihang University, Hangzhou, 311115, China; School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China. Electronic address:
To enhance adaptability in practical applications, active disturbance rejection control is extended to uncertain nonaffine systems with input saturation, achieving practical prescribed-time tracking control that drives the error to a sufficiently small neighborhood within a user-defined settling time. Based on a time-varying function called time base generators (TBG), a more general practical prescribed-time (PPT) nonlinear extended state observer (NLESO) is designed to estimate unknown states and total uncertainties while effectively suppressing peaking phenomena. By utilizing the PPT NLESO outputs, a TBG-based saturation control law is constructed to achieve PPT output tracking and address the nonaffine-in-control problem.
View Article and Find Full Text PDFThis article aims to address the formation control issue of the unknown nonaffine nonlinear heterogeneous multiagent system (MAS) considering formation tracking accuracy and computational cost. A novel dynamic prescribed boundary-based event-triggered mechanism is proposed first to flexibly adjust the emphasis on these two indicators, and applied to data modeling and controller design simultaneously to reduce their computational cost. On one hand, an observer-based pseudo gradient estimation algorithm is designed under event-triggered framework for model reconfiguration with only input/output data of system rather than mathematical dynamics.
View Article and Find Full Text PDF