scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

2020 ◽  
Vol 42 (11) ◽  
pp. 1923-1934 ◽  
Author(s):  
Rongrong Wang ◽  
Yangchun Wei ◽  
Ronghu Chi
Keyword(s):  
Discrete Time ◽  
Data Model ◽  
Iterative Learning Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Time Interval

In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller design, an iterative dynamic linear data model is built between two consecutive iterations to formulate the linear input-output data relationship of the repetitive nonlinear nonaffine discrete-time system. The linear data model is virtual and does not have any physical meanings, which is very different to the traditional mechanism mathematical model. In the sequel, the eDDOILC is proposed by designing an objective function with respect to the proposed two-dimensional ISMS. Rigorous proof is provided to show the convergence of the proposed eDDOILC method. Furthermore, the results have been extended to a multiple-input multiple-output (MIMO) nonaffine nonlinear discrete-time repetitive system. In general, the proposed eDDOILC is data-driven where no explicit model information is included. It is illustrated that the presented eDDOILC is effective when applied to the nonlinear nonaffine uncertain systems.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

2020 ◽  
Vol 42 (12) ◽  
pp. 2166-2177
Author(s):  
Gaoyang Jiang ◽  
Zhongsheng Hou
Keyword(s):  
Control Problem ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Time Of Arrival ◽  
Multiple Time ◽  
Data Driven Approach ◽  
Along Track

Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

Lateral robust iterative learning control for unmanned driving robot vehicle

2020 ◽  
Vol 234 (7) ◽  
pp. 792-808
Author(s):  
Shuhua Su ◽  
Gang Chen
Keyword(s):  
Dynamic Model ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Steering System ◽  
Path Tracking ◽  
Iterative Learning ◽  
Control Law ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.

scholarly journals Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Yun-Shan Wei ◽  
Qing-Yuan Xu
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Initial State ◽  
Tracking Errors ◽  
Discrete Time Systems ◽  
P Type ◽  
Time Systems

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

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