Article
Automation & Control Systems
Lanlan Su
Summary: The study focuses on robust monotonic convergent iterative learning control for uncertain linear systems, deriving an ILC algorithm that optimizes convergence speed. It establishes robust monotonic convergence through the positive definiteness of a matrix polynomial and proposes a necessary and sufficient condition in the form of sum of squares for positive definiteness, amendable to linear matrix inequalities. The optimal ILC algorithm maximizing convergence speed is obtained by solving a set of convex optimization problems, allowing flexibility in choosing the order of the learning function for algorithm complexity.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Mathematics
Lei Wang, Liangxin Dong, Ruitian Yang, Yiyang Chen
Summary: This paper systematically studies the monotonic convergence of the corresponding dynamic iterative learning controller for discrete linear repetitive processes with different relative degrees. It presents a 2D discrete Roesser model of the control system and analyzes the monotonic convergence condition of the controlled system. It also provides sufficient conditions for the existence of the controller and validates the effectiveness through simulation results.
Article
Computer Science, Information Systems
Ijaz Hussain, Xiaoe Ruan, Chen Liu, Yan Liu
Summary: This article focuses on the modeling of repetitive finite-length linear discrete-time singular systems and the optimization of gain for iterative learning control. By adjusting the learning gain vector in minimizing the norm of tracking error and compensation vector, the linearly monotonic convergence of tracking error is derived. A robust quasi-scheme is proposed for addressing system parameter uncertainties.
Article
Automation & Control Systems
Wenjie Mei, Denis Efimov, Rosane Ushirobira, Alexander Aleksandrov
Summary: The convergence conditions for a class of generalized Persidskii systems and their discretized dynamics are introduced and can be tested through linear inequalities. The case of almost periodic convergence for this class of systems with almost periodic input is also studied. The proposed results are applied to a Lotka-Volterra model and opinion dynamics.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Mojtaba Ayatinia, Mehdi Forouzanfar, Amin Ramezani
Summary: This paper presents a new robust convergence condition for linear multivariable discrete-time systems with iteration-varying uncertainty using iterative learning control with initial state learning (ILC-ISL). The proposed method is based on linear matrix inequality (LMI) and provides fixed learning gains during time and iteration. The effectiveness of the approach is demonstrated through numerical examples and a mechanical system.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Deyuan Meng, Jingyao Zhang
Summary: This article introduces a system equivalence transformation method for robust iterative learning control, addressing the contradiction in convergence conditions and simplifying the control of system signals while ensuring the convergence of output tracking errors. Simulation examples are provided to validate the established robust ILC results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Lorenzo Carbone, Mario Marchesoni, Massimiliano Passalacqua, Luis Vaccaro, Andrea Formentini
Summary: This article introduces a new control algorithm for matrix converters based on full-state feedback, utilizing an H-2-linear matrix inequality (LMI) framework to expand the system stability region and introducing a Kalman filter for full system state estimation without the need for additional sensors. The method has a limited computational burden and does not require a high performance control platform. Experimental tests compare the proposed strategy with a previous stabilization approach in terms of stability performance and power quality.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Mojtaba Ayatinia, Mehdi Forouzanfar, Amin Ramezani
Summary: This paper presents a new robust convergence condition for iterative learning control (ILC) in the presence of iteration-varying uncertainty. The proposed method, based on linear matrix inequality (LMI), provides a fixed learning gain over time and iteration. The effectiveness of the method is evaluated through two numerical examples.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Jianan Wang, Xiangjun Ding, Chunyan Wang, Li Liang, Han Hu
Summary: This paper investigates the affine formation control problem for multi-agent systems with prescribed convergence time. A distributed continuous control algorithm and a distributed control protocol utilizing a leader-follower strategy are proposed, with the boundary layer technique used to avoid chattering effect. Simulation examples demonstrate the effectiveness of the proposed design.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Lin Xiao, Wentong Song, Xiaopeng Li, Lei Jia, Jiayue Sun, Yaonan Wang
Summary: In this article, a predefined-time convergent and integral-enhanced zeroing neural network (PCIE-ZNN) model is proposed for efficiently solving the time-variant linear matrix inequality (LMI) under nonideal conditions. Through mathematical analysis and numerical simulations, the PCIE-ZNN model is shown to have better convergence and robustness even in the presence of noise interference.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Multidisciplinary
Zhen Li, Yang Tang, Jian-an Fang, Tingwen Huang
Summary: This paper investigates the impact of orientation noises on the formation control problem, using convex polytopes and martingale sequences to tackle the effect of orientation noises. A sufficient criterion is derived to ensure the specified formation shape, and a numerical example is provided to illustrate the derived result.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Automation & Control Systems
Leopoldo Jetto, Valentina Orsini, Raffaele Romagnoli
Summary: This article introduces a new approach based on a two degrees of freedom control scheme to simplify the complexity of stability and feasibility analysis of MPC and reduce the complexity of relative optimization procedures. The method computes the input through online minimization of a quadratic cost functional and applies it to the closed-loop system. By assuming the input forcing the system to be given by a B-spline function, the constrained optimization problem is greatly simplified.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Yan Liu, Xiaoe Ruan
Summary: This paper develops a parameter-optimal iterative learning control (POILC) scheme for linear discrete-time-invariant systems with Markov parameters, and rigorously analyzes its robustness to system parameter uncertainties. Numerical simulations validate the effectiveness of the proposed approach.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Engineering, Multidisciplinary
Zhen Li, Yang Tang, Yongqing Fan, Tingwen Huang, Leszek Rutkowski
Summary: This paper investigates the impact of inaccurate sensing results on formation control and approximates rotation behaviors under the influence of constrained mismatched compasses using convex polytopes. The results show that all agents can converge to the desired formation shape rapidly, regardless of the alignment of their directions.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Deyuan Meng
Summary: This article proposes an observer-based iterative method to bring control design into mathematics for solving linear algebraic equations (LAEs). The relationship between solving LAEs and designing observer-based control systems is revealed, and an iterative method for solving LAEs is developed based on the design of basic state observers. Different selections of initial conditions can determine the (least squares) solutions for any (un)solvable LAEs exponentially fast or monotonically. The general solution subspace and particular (least squares) solutions of LAEs are closely related to the unobservable subspace and observable states of their associated observer systems, respectively. By incorporating the design idea of deadbeat control, the solving of LAEs can be achieved within finite iterations. The proposed iterative method can be used to develop a new observer-based design algorithm for traditional two-dimensional iterative learning control to achieve perfect tracking tasks.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
