Article
Automation & Control Systems
Wei Xiao, Calin Belta, Christos G. Cassandras
Summary: This article presents an adaptive CBFs (aCBFs) approach that can handle time-varying control bounds and noise in system dynamics, and compares its advantages with existing CBF techniques.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Wei Xiao, Calin Belta
Summary: This paper addresses the problem of stabilizing a dynamical system while optimizing costs and satisfying safety constraints and control limitations. By introducing high-order control barrier functions (HOCBFs) and control Lyapunov functions (CLFs), a quadratic program-based approach is proposed to solve the optimal control problem, and two methods are proposed to address the feasibility problem. Finally, the extension of this methodology for safe navigation in unknown environments is demonstrated.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Juan G. Rueda-Escobedo, Jaime A. Moreno
Summary: This paper presents strong Lyapunov functions for two classical problems in adaptive control and parameter identification. These Lyapunov functions incorporate the classical persistency of excitation conditions in their structure, allowing to demonstrate global uniform asymptotic stability of the associated adaptive systems under sufficient and necessary conditions.
Article
Mathematics, Applied
N. K. Arutyunova, A. M. Dulliev, V. I. Zabotin
Summary: This research introduces two algorithms for solving global optimization problems, which belong to the family of non-uniform covering methods and are designed for objective functions satisfying the Vanderbei condition. The algorithms' performance is illustrated through multiple test numerical examples, including non-Lipschitz continuous objective functions.
JOURNAL OF APPLIED MATHEMATICS AND COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Qiong Liu, Dongyu Li, Shuzhi Sam Ge, Ye Guo
Summary: This paper introduces two adaptive feedforward RBFNN control frameworks: the reduced lattice scheme and the optimized scheme. These frameworks can better learn the weights of hidden nodes, leading to improved performance of the neural network.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Civil
Liqi Wang, Jiuxiang Dong
Summary: This paper addresses the safety-critical tracking control problem of uncertain nonlinear systems and proposes an integral concurrent learning high order control barrier function and an ICL control Lyapunov function to solve this problem. The research results demonstrate that these methods can provide strict safety guarantee and overcome the difficulty of converting control constraints.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Jia Guo, Sai Tej Paruchuri, Andrew J. Kurdila
Summary: An adaptive nonparametric method is proposed in this article to estimate unknown scalar-valued functions in systems governed by ordinary differential equations (ODEs). The convergence analysis is facilitated by introducing a novel condition of partial persistent excitation (partial PE), and the effectiveness of the research results is illustrated through numerical simulations.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Chemical
Chen Chen, Jiangang Lu
Summary: This study proposes a novel different-factor full-form model-free adaptive controller (DF-FFMFAC) for the multivariable control of dividing wall columns (DWCs). The controller achieves encouraging control performance and proves to be a promising data-driven method according to simulation results.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Computer Science, Information Systems
Dan Bao, Xiaoling Liang, Shuzhi Sam Ge, Zhiwei Hao, Baolin Hou
Summary: This paper introduces a framework for adaptive fuzzy control and optimization of nonlinear systems under uncertainties and disturbances. The barrier Lyapunov function (BLF) technique is used to determine output constraints. Fuzzy logic systems (FLSs) are applied to approximate nonlinear terms and improve tracking performance. Bayesian optimization and particle swarm optimization (BO-PSO) are combined for gains optimization to enhance control performance. Multilayer neural networks (MNNs) are employed as surrogate models of nonlinear systems with interval parameters to improve the computational efficiency of the optimization process. Two simulations are conducted to demonstrate the effectiveness of the proposed framework.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Martin Steinberger, Martin Horn, Antonella Ferrara
Summary: A discrete-time adaptive control approach for uncertain linear multivariable networked systems is proposed in this study, which can handle unknown time delays and reduce conservatism effectively. Simulation examples show that the proposed technique outperforms a nonadaptive algorithm.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Bailing Tian, Zhiyu Li, Xiaopeng Zhao, Qun Zong
Summary: In this paper, a novel adaptive multivariable control algorithm is proposed for reusable launch vehicle (RLV). The algorithm is simple and easy to implement, and utilizes Lyapunov-based technique to guarantee error confinement within prescribed regions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Lihui Ding, Jinlin Sun, Shihong Ding, Zhiqiang Pu, Jianqiang Yi
Summary: This study proposes an appointed-time prescribed performance control design for uncertain nonlinear systems using adaptive fuzzy technique. The controller achieves appointed-time tracking control for higher-order nonlinear systems with uncertainties bounded by positive functions. Barrier Lyapunov functions integrated with appointed-time prescribed performance functions are designed to cope with the requirements of output constraints and quantitative performance constraints. Adaptive fuzzy systems are constructed to approximate the unknown continuous upper-bound functions of the uncertainties. A novel adaptive fuzzy appointed-time control scheme is proposed for the uncertain nonlinear system bounded by positive functions through the backstepping framework. The appointed settling time, predetermined steady-state error, and quantitative transient-state performance of the closed-loop system are ensured. The fixed-time convergence of the closed-loop system is proved via Lyapunov analysis, and comparative simulations are conducted to illustrate the superiority of the proposed scheme.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Sasa Rakovic
Summary: This article examines the application of the control Lyapunov inequality in unconstrained and constrained linear discrete time systems, proposing a novel necessary and sufficient condition for Minkowski functions that satisfy the inequality and studying the topological properties of the control map. Additionally, the novel results derived in the unconstrained setting are extended to the constrained setting.
Article
Engineering, Marine
Enver Tatlicioglu, Bayram Melih Yilmaz, Aydogan Savran, Musa Alci
Summary: This article focuses on the tracking control of surface vessels with uncertainties in the dynamical model. A model independent strategy is used, where the dynamical uncertainties are modeled using a fuzzy logic network. The controller design includes proportional derivative feedback and self-adjusting fuzzy logic compensation. Stability analysis based on Lyapunov method is performed to ensure semi-global practical tracking. Numerical simulations demonstrate the effectiveness of the proposed method.
Article
Computer Science, Artificial Intelligence
B. Melih Yilmaz, Enver Tatlicioglu, Aydogan Savran, Musa Alci
Summary: The research proposes a repetitive learning control method fused with adaptive fuzzy logic techniques for industrial robotic manipulators. Modeling uncertainties are addressed using a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning. Stability is ensured through Lyapunov type techniques, demonstrating the efficacy of the control methodology for robot manipulators.
APPLIED SOFT COMPUTING
(2021)