scholarly journals Iterative Learning Control Based Fractional Order PID Controller for Magnetic Levitation System

2019 ◽  
Vol 38 (4) ◽  
pp. 885-900
Author(s):  
Bushra Hanif ◽  
Inam-ul-Hassan Shaikh ◽  
Ahsan Ali
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Magnetic Levitation ◽  
Learning Control ◽  
Iterative Learning ◽  
Physical Contact ◽  
Control Input ◽  
Stabilizing Controller ◽  
Wide Range ◽  
Non Linear

Maglev (Magnetic Levitation) systems are an interesting class of systems since they work without any physical contact and are hence frictionless. Due to this attractive property, such systems have the potential for wide range of applications such as maglev trains. Maglev is non-linear due to magnetic field and unstable that suggest the need of stabilizing controller. An appropriate controller is required to levitate the object at desired position. FOPID (Fractional Order Proportional Integral Derivative) controller and ILC (Iterative learning Control) based FOPID controller are designed in this paper for the levitation of metallic ball with desired reference at minimum transient errors. Since maglev is unstable and ILC is used only for stable systems, FOPID controller is used to stabilize the plant. Non-linear interior point optimization method is used to obtain the parameters of FOPID controller. An ILC is used as a feedforward controller in order to improve the response iteratively. P, PD and PID-ILC control laws are used to update the new control input in ILC based FOPID controller. The overall control scheme is therefore a hybrid combination of ILC and FOPID. The effectiveness of proposed technique is analyzed based on performance indices via simulation. ISE (Integral Square Error) and IAE (Integral Absolute Error) is lesser in case of hybrid PID-ILC as compared to simple FOPID controller.

Robust adaptive sliding mode control combination with iterative learning technique to output tracking of fractional-order systems

2017 ◽  
Vol 40 (6) ◽  
pp. 1808-1818 ◽  
Author(s):  
Ehsan Ghotb Razmjou ◽  
Seyed Kamal Hosseini Sani ◽  
Jalil Sadati
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Sliding Mode ◽  
External Disturbance ◽  
Learning Control ◽  
Iterative Learning ◽  
Adaptive Sliding Mode Control ◽  
Order System ◽  
Control Input ◽  
Fractional Order Systems

This paper develops a novel controller called adaptive iterative learning sliding mode (AILSM) to control linear and nonlinear fractional-order systems. This controller applies a hybrid structure of adaptive and iterative learning control in to sliding mode method. It can switch between both adaptive and iterative learning control in order to use the advantages of both controllers simultaneously and therefore achieve better control performance. This controller is designed in a way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order system. The proposed controller, unlike the conventional iterative learning control, does not need to apply direct control input to output of the system. It is shown that the controller performs well in partial and complete observable conditions. Illustrative examples verify the performance of the proposed control in presence of unknown disturbances and model uncertainties.

Iterative learning control for a class of non-linear switched systems

2013 ◽  
Vol 7 (3) ◽  
pp. 470-481 ◽  
Author(s):  
Xuhui Bu ◽  
Ziyi Fu ◽  
Fashan Yu ◽  
Zhongsheng Hou
Keyword(s):  
Switched Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Non Linear

scholarly journals Fractional-order iterative learning control for singular fractional- order system: (P) - PDa type

2016 ◽  
Vol 66 (3) ◽  
pp. 40-49 ◽  
Author(s):  
Mihailo Lazarevic ◽  
Nikola Djurovic ◽  
Bosko Cvetkovic ◽  
Petar Mandic ◽  
Ljubisa Bucanovic
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Fractional Order System ◽  
Order System ◽  
System P

scholarly journals Motion Planning and Iterative Learning Control of a Modular Soft Robotic Snake

2020 ◽  
Vol 7 ◽  
Author(s):  
Ming Luo ◽  
Zhenyu Wan ◽  
Yinan Sun ◽  
Erik H. Skorina ◽  
Weijia Tao ◽  
...  
Keyword(s):  
Motion Planning ◽  
Disaster Response ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Curvature Sensing ◽  
Important Research Topic ◽  
Wide Range ◽  
Constrained Environments ◽  
High Degree

Snake robotics is an important research topic with a wide range of applications, including inspection in confined spaces, search-and-rescue, and disaster response. Snake robots are well-suited to these applications because of their versatility and adaptability to unstructured and constrained environments. In this paper, we introduce a soft pneumatic robotic snake that can imitate the capabilities of biological snakes, its soft body can provide flexibility and adaptability to the environment. This paper combines soft mobile robot modeling, proprioceptive feedback control, and motion planning to pave the way for functional soft robotic snake autonomy. We propose a pressure-operated soft robotic snake with a high degree of modularity that makes use of customized embedded flexible curvature sensing. On this platform, we introduce the use of iterative learning control using feedback from the on-board curvature sensors to enable the snake to automatically correct its gait for superior locomotion. We also present a motion planning and trajectory tracking algorithm using an adaptive bounding box, which allows for efficient motion planning that still takes into account the kinematic state of the soft robotic snake. We test this algorithm experimentally, and demonstrate its performance in obstacle avoidance scenarios.

scholarly journals Design and Analysis of Fractional Order PD α -Type Iterative Learning Control

2017 ◽  
Vol 50 (1) ◽  
pp. 8077-8083 ◽  
Author(s):  
Yang Zhao ◽  
Fengyu Zhou ◽  
Da Wang ◽  
Yan Li
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning
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