Further results on advanced robust iterative learning control and modeling of robotic systems

2020 ◽  
pp. 095440622096599
Author(s):  
Mihailo P Lazarević ◽  
Petar D Mandić ◽  
Srđan Ostojić
Keyword(s):  
Iterative Learning Control ◽  
Feedback Linearization ◽  
Angular Displacement ◽  
Viscous Friction ◽  
Learning Control ◽  
Iterative Learning ◽  
Revolute Joints ◽  
General Order ◽  
Robotic Joints ◽  
The Time Domain

Recently, calculus of general order [Formula: see text] has attracted attention in scientific literature, where fractional operators are often used for control issues and the modeling of the dynamics of complex systems. In this work, some attention will be devoted to the problem of viscous friction in robotic joints. The calculus of general order and the calculus of variations are utilized for the modeling of viscous friction which is extended to the fractional derivative of the angular displacement. In addition, to solve the output tracking problem of a robotic manipulator with three DOFs with revolute joints in the presence of model uncertainties, robust advanced iterative learning control (AILC) is introduced. First, a feedback linearization procedure of a nonlinear robotic system is applied. Then, the proposed intelligent feedforward-feedback AILC algorithm is introduced. The convergence of the proposed AILC scheme is established in the time domain in detail. Finally, simulations on the given robotic arm system confirm the effectiveness of the robust AILC method.

Non-linear contour tracking using feedback PID and feedforward position domain cross-coupled iterative learning control

2017 ◽  
Vol 40 (6) ◽  
pp. 1970-1982 ◽  
Author(s):  
Jie Ling ◽  
Zhao Feng ◽  
Daojin Yao ◽  
Xiaohui Xiao
Keyword(s):  
Time Domain ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Contour Error ◽  
Contour Tracking ◽  
Vector Method ◽  
Non Linear ◽  
And Performance ◽  
The Time Domain

In this paper, a position domain cross-coupled iterative learning controller combining proportional–integral–derivative (PID)-type iterative learning control (ILC) and proportional–derivative (PD)-type cross-coupling control (CCC) is presented aiming at non-linear contour tracking in multi-axis motion systems. Traditional individual control methods in the time domain suffer from poor synchronization of relevant motion axes. The complicated computation of coupling gains in CCC and cross-coupled ILC (CCILC) restricts their applications for non-linear contour. The proposed position domain CCILC (PDCCILC) approach introduces a position domain design concept into CCILC to improve synchronization and performance for non-linear contour tracking and it relies less on the accuracy of coupling gains than conventional CCILC. The stability and performance analysis are conducted using a lifted system representation. The contour error vector method is applied to estimate the coupling gains in simulations and experiments. Simulation and experimental results of three typical non-linear contour tracking cases (i.e. semi-circle, parabola and spiral) based on a two-axis micro-motion stage demonstrate superiority and efficacy of the proposed feedback PID and feedforward PDCCILC compared with existing ILC and CCILC in the time domain.

scholarly journals INTELLIGENT FRACTIONAL ORDER ITERATIVE LEARNING CONTROL USING FEEDBACK LINEARIZATION FOR A SINGLE-LINK ROBOT

2017 ◽  
Vol 18 (1) ◽  
pp. 155-176
Author(s):  
Iman Ghasemi ◽  
Abolfazl Ranjbar Noei ◽  
Jalil Sadati
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Feedback Linearization ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Order Derivative ◽  
Error Signal ◽  
Loop Control ◽  
Fractional Order Derivative

In this paper, iterative learning control (ILC) is combined with an optimal fractional order derivative (BBO-Da-type ILC) and optimal fractional and proportional-derivative (BBO-PDa-type ILC). In the update law of Arimoto's derivative iterative learning control, a first order derivative of tracking error signal is used. In the proposed method, fractional order derivative of the error signal is stated in term of 'sa' where  to update iterative learning control law. Two types of fractional order iterative learning control namely PDa-type ILC and Da-type ILC are gained for different value of a. In order to improve the performance of closed-loop control system, coefficients of both  and  learning law i.e. proportional , derivative  and  are optimized using Biogeography-Based optimization algorithm (BBO). Outcome of the simulation results are compared with those of the conventional fractional order iterative learning control to verify effectiveness of BBO-Da-type ILC and BBO-PDa-type ILC

scholarly journals Iterative Learning Control for Fractional order Nonlinear System with Initial Shift

2021 ◽  
Author(s):  
Zhou Fengyu ◽  
Wang Yugang
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Closed Loop ◽  
Learning Control ◽  
Iterative Learning ◽  
Fractional Order Systems ◽  
Tracking Errors ◽  
Fractional System ◽  
The Time Domain ◽  
Fractional Order Nonlinear System

Abstract In this study, a closed-loop Dα -type iterative learning control(ILC) with a proportional D-type iterative learning updating law for the initial shift is applied to nonlinear conformable fractional system. First, the system with the initial shift is introduced. Then, fractional-order ILC (FOILC) frameworks that experience the initial shift problem for the path-tracking of nonlinear conformable fractional order systems are addressed. Moreover, the sufficient condition for the convergence of tracking errors is obtained in the time domain by introducing λ -norm and Hölder’s inequality. Lastly, numerical examples are provided to illustrate the effectiveness of the proposed methods.

The Effect of Iterative Learning Control on the Force Control of a Hydraulic Cushion

2020 ◽  
Author(s):  
Ignacio Trojaola ◽  
Iker Elorza ◽  
Eloy Irigoyen ◽  
Aron Pujana-Arrese ◽  
Carlos Calleja
Keyword(s):  
Force Control ◽  
Iterative Learning Control ◽  
Feedback Linearization ◽  
Reference Signal ◽  
Learning Control ◽  
Iterative Learning ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Valve Dynamics

Abstract An iterative learning control (ILC) algorithm is presented for the force control circuit of a hydraulic cushion. A control scheme consisting of a PI controller, feed-forward (FF) and feedback-linearization is first derived. The uncertainties and nonlinearities of the proportional valve, the main system actuator, prevent the accurate tracking of the pressure reference signal. Therefore, an extra ILC FF signal is added to counteract the valve model uncertainties. The unknown valve dynamics are attenuated by adding a fourth-order low-pass filter to the iterative learning control design, which is split up into two second-order low-pass filters to carry out forward and backward filtering and obtain zero-phase filtering. The addition of the ILC signal presents significant improvements in terms of settling time and overshoot of the pressure signal in the cylinder.

scholarly journals The advanced iterative learning control algorithm for rehabilitation exoskeletons

2020 ◽  
Vol 70 (3) ◽  
pp. 29-34
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković
Keyword(s):  
Iterative Learning Control ◽  
Feedback Linearization ◽  
Control Algorithm ◽  
Controller Design ◽  
Control Object ◽  
Biomechanical Model ◽  
Learning Control ◽  
Iterative Learning ◽  
Outer Loop ◽  
Feedforward Controller

In this paper an advanced iterative learning control algorithm for rehabilitation exoskeletons is proposed. A simplified biomechanical model is used as the control object to verify control algorithm feasibility. The control design is proposed as two level controller consisting of inner and outer loop. In the inner loop the feedback linearization is applied to cancel out the model nonlinearities. In the outer loop the advanced iterative learning control algorithm of sgnPDD2 type is applied as a feedforward controller and classical PD controller as a feedback controller. Uncertainties are added in order to examine the controller design robustness. Numerical simulation is carried out.

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