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Iterative Learning Control for Torque Ripple Minimization of Switched Reluctance Motor Drive
Published in Bogdan M. Wilamowski, J. David Irwin, Control and Mechatronics, 2018
Sanjib Kumar Sahoo, Sanjib Kumar Panda, Jian-Xin Xu
Consider a control task that requires perfect tracking in a finite interval when both the control reference and disturbance are repeatable. Most control methods, including adaptive or robust control, may not be suitable for such a class of tasks for two reasons. First, these control methods are characterized by asymptotic convergence, thus it is difficult to guarantee a perfect tracking even if the initial discrepancy is zero. Second and more importantly, those control methods are not able to learn from the previous task execution that may succeed or fail. Without learning, a control system can only produce the same performance without improvement even if the task is repeatedly executed. ILC was proposed to meet this kind of control requirement. The idea of ILC is straight forward: using control information of preceding execution to improve the present execution. This is realized through memory-based learning [11]. ILC-based torque control scheme had been used for other motor drives such as PMSM [11,12]. ILC control design has to satisfy initial resetting conditions, i.e., the actual phase torque matches the desired phase torque at the start (time t = 0) for every iteration.
Two Decades of Research on Linear Repetitive Processes
Published in Krzysztof Gałkowski, Jeff David Wood, Multidimensional Signals, Circuits and Systems, 2001
In essence. ILC is a technique to control systems operating in a repetitive mode with the additional requirement that a specified output trajectory r(t) on a finite interval [О, Г] is followed to a high precision. Examples of such systems are robot manipulators required to repeat a given task, chemical batch processes, or. more generally, the class of tracking systems. Motivated by human learning, the basic idea of ILC is to use information from previous executions of the task in order to improve performance from trial to trial in the sense that the tracking егтог is sequentially reduced — sec (Arimolo et al. 1984. Moore 1993) for further background and early work in this general area.
Singleton Type 1 Fuzzy Logic Systems: No Uncertainties
Published in Abhijit Pandit, Mathematical Modeling using Fuzzy Logic, 2021
Iterative learning control (ILC) improves the performance of a control system (CS) by repeatedly using past wisdom in CS operations. The ILC system iteratively solves the parameter optimization learning problem that minimizes the objective function that specifies CS performance. One way to look at fuzzy tenancy is as a user-friendly early nonlinear assistant dealing with rippled or poorly defined vegetation designed in a heuristic way that incorporates human technology but no universal diamond method.
p-Accelerated normal S-iterative learning control algorithm for linear discrete singular time-delay systems
Published in International Journal of General Systems, 2023
D. R. Sahu, Nitish Kumar Singh
On the other hand, time delay is a common occurrence in practical control problems such as remote-controlled robots, batch processes and man-machine systems. Unfortunately, the presence of time delay often degrades the performance of the control system and can even lead to instability of the entire system (He et al. 2022; Niculescu 2001). Fortunately, Iterative Learning Control (ILC) has emerged as a promising solution for addressing control problems in systems with time delay. ILC involves iterative adjustments of control inputs within fixed time intervals. The algorithm was initially applied to continuous-time singular systems with state delay, and subsequent studies analyzed the convergence and state tracking capabilities of the algorithm (Xie, Xie, and Liu 1999). Further research by Ting, Senping, and Yanbo (2014) focused on ILC problems in continuous-time singular systems with multiple time delays. In recent years, the mathematics and control community has shown considerable interest in discrete singular time-delay systems, which are frequently encountered in dynamic input-output economic systems (Shao et al. 2014).
Hybrid Arrangement of Iterative Learning Control Strategy for Ball and Beam System
Published in IETE Journal of Research, 2023
P. Ravichandran, S. Sathiyavathi, S. Sathish Babu, A. Vimala Starbino
Iterative learning control (ILC) is an intelligent control tool for improving the transient response, tracking performance, and stability of the systems performing a repeating task. The key idea of ILC is that it incorporates the error information from previous iterations into the control for subsequent iterations [1]. By doing so, the high performance of the system is achieved with low transient tracking error despite large model uncertainty and repetitive disturbances [2]. The foremost advantage of ILC is that it anticipates and responds to the exogenous signals in advance by learning from the previous iterations. Several researchers have contributed their findings towards the development of ILC and applied it in many fields such as robots, rotary systems, chemical processes, biomedical systems, actuators, and nonlinear systems [3].
Robust iterative learning control for iteration- and time-varying disturbance rejection
Published in International Journal of Systems Science, 2020
Chengyuan Tan, Sen Wang, Jing Wang
Many studies have shown that the iteration-varying disturbance has bad influence on ILC. Because of the existence of the special disturbances, the characteristics of the system in different batches are not the same as previous, which makes the applicable premise of traditional iterative learning can't be satisfied. Some progress have been made in the research of rejecting iteration-varying disturbances in ILC. A two-dimensional -based method is designed to overcome some problems of a class of linear discrete-time ILC system (Zhu, Hu, & Liu, 2015). There problems include the iteration-varying parametric uncertainties, disturbances, measurement noises. Because of the non-repeatability of the disturbances in iteration process, the system can not only rely on the information of the previous batch to modify the control law, so Chen Yangquan and other researchers proposed using higher-order ILC to design controller (Chen & Moore, 2002; Moore & Chen, 2003; Svante & Norrlof, 2006; Wang, Dong, & Wang, 2016). These methods consider more information of the previous batches to reduce influence of disturbances. They restrain the effect of the iteration-varying interference and achieve good control effect for unknown systems.