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Discrete-Time Adaptive Iterative Learning Control with Unknown Control Directions
Miao Yu,Jianliang Zhang,Donglian Qi 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.6
An adaptive iterative learning control scheme is proposed for a class of discrete-time nonlinear systems with random initial conditions and iteration-varying desired trajectories. The discrete Nussbaum gain method is incorporated into the control design to tackle the problem associated with the lack of a priori knowledge of the control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis. The effectiveness of the proposed control law is veri-fied through numerical simulation.
Formation Control of Discrete-Time Multi-Agent Systems by Iterative Learning Approach
Yang Liu,Yingmin Jia 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.5
In this paper, the formation control problem is investigated for discrete-time multi-agent systems with unknown nonlinear dynamics by means of the iterative learning approach. For networks with switching topology, a distributed iterative learning scheme is developed using the local formation error data with anticipation in time, and a sufficient condition is derived to guarantee that the desired formation can be preserved during the whole finite-time motion or operation process, even in the pres-ence of initial formation errors. Simulation results illustrate the effectiveness of the proposed method.
Qing-Yuan Xu,Jing Cheng,Yun-Shan Wei,Kai Wan 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.9
In this paper, an adaptive iterative learning control (ILC) design method is proposed for a class of nonlinear discrete-time systems with nonaffine structure, randomly varying trail length, and uncertain control direction. In order to achieve repetitive tracking control of the nonaffine structure systems with uncertain control direction, randomly varying trail length, and other uncertainties, we apply a high-order neural network to approximate the expected system input. Then, a novel adaptation law is designed for the neural network weight vector. The main feature of the method proposed in this paper is that the weight vector norm instead of the weight vector itself is updated iteratively to realize the successive approximation of the expected system input, the custom-designed identification mechanism is not necessary to deal with the uncertain control direction, and the analysis of randomly varying trail lengths problem is strictly established. The convergence of the proposed adaptive ILC is set up by a composite energy function. The effectiveness of the proposed adaptive ILC design is validated by two simulation examples.
Robustness of 2nd-order Iterative Learning Control for a Class of Discrete-Time Dynamic Systems
Yong-Tae Kim(김용태) 한국지능시스템학회 2004 한국지능시스템학회논문지 Vol.14 No.3
In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.
Teng-Fei Xiao,Xiao-Dong Li,John K. L. Ho 제어·로봇·시스템학회 2015 International Journal of Control, Automation, and Vol.13 No.1
In this article, a novel fuzzy systems based on adaptive Iterative Learning Control (ILC) strategy is presented to deal with a class of non-parametric nonlinear discrete-time systems which perform iteration-varying reference trajectory tracking. Using the technique of fuzzy systems to compensate for the non-parametric uncertainty of the discrete-time system dynamics, the proposed adaptive ILC scheme can well track the iteration-varying reference trajectory beyond the initial time points. The convergence of the fuzzy systems based adaptive ILC algorithm is guaranteed by theoretical analysis, and a numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.
Kai Wan,Yun-Shan Wei 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.1
Most of adaptive iterative learning control (AILC) algorithms focus on one-dimensional (1-D) systems,rather than two-dimensional (2-D) systems. This brief is first concerned with AILC for 2-D nonlinear discrete timevarying Fornasini-Marchesini system (NDTVFMS) with nonrepetitive reference trajectory under iteration-varyingboundary states. By using Lyapunov analysis method, it can guarantee that the ultimate tracking error tends to zeroasymptotically, and make all identified parameters and system signals to be bounded as iteration number goes toinfinity. Two illustrative examples are used to validate the effectiveness of the designed AILC approach
An Optimal Approach to Online Tuning Method for PID Type Iterative Learning Control
Furqan Memon,Cheng Shao 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.8
The proportional-integral-derivative (PID) controller is widely used in process control engineering. However, the parameter updating of PID controller has been a challenging issue for control engineers. A new approach to apply iterative learning control (ILC) scheme for updating the PID parameters, is presented in this paper. The quadratic performance index is employed to optimize the parameters of the PID controller and then an optimal PID type iterative learning control (ILC) scheme is established for discrete linear time-invariant (LTI) systems. In addition, the convergence analysis of optimal ILC of PID type is well described by using Lyapunov composite energy function. The tracking performance of the desired output can be enhanced by the proper choice of penalty matrices. The resultant performance using proposed methodology is significantly improved in term of convergence as compared to available methods in the literature. Simulation examples are also given also to demonstrate the effectiveness of the proposed scheme.
Kai Wan,Xiao-Dong Li 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4
Almost all existing iterative learning control (ILC) algorithms have focused on one-dimensional (1-D) dynamical systems, and seldom were designed for multidimensional systems. In this article, a two-gain ILC law is presented to deal with the ILC issue of two-dimensional (2-D) linear discrete systems described by the first Fornasini-Marchesini model (FMMI). Convergence and robustness of the proposed ILC law under two different cases of boundary conditions are discussed, respectively. A super-vector technique is used to transfer the ILC process of 2-D FMMI into a 2-D Roessor model such that sufficient convergence/robustness conditions of the proposed ILC law are derived. An illustrative example is given to validate the effectiveness of the proposed ILC approach.
Suolin Duan,Yonghong Wu,Zhenghua Ma,Guirong Lu 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper a discrete closed-loop D-type iterative learning control (ILC) scheme for the electrohydraulic position servo systems with parameter uncertainties is presented. The conditions which guarantee the convergence of the algorithm are given and the convergence is proved. The simulation results demonstrate the effectiveness of the presented D-type ILC algorithm for the discretization electrohydraulic position servo systems with parameter uncertainties.
Robust Optimal PID type ILC for Linear Batch Process
Furqan Memon,Cheng Shao 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.2
The proportional-integral-derivative (PID) controller is standard technique for controlling the industrial batch process. However, the parameter tuning and updating of PID controller has been a challenging topic for control engineers especially for the real system having initial state error, state and output disturbances. In this paper, a kind of robust optimal iterative learning control (ILC) scheme is suggested to update the PID gains for the linear system with initial state error, state and output disturbances. The quadratic performance criteria is considered, which constitute of the error and input slew rate, and the robust BIBO stability is investigated theoretically for the proposed PID type ILC scheme. In addition, the bound of the tracking error has been calculated by using Lyapunov composite energy function. Simulation examples are also given to demonstrate the effectiveness of the proposed scheme in term of its ability to deal with linear as well as nonlinear system.