Improved Feed-Forward Control in Track Seeking Using Iterative Learning Control

2016 ◽  
Author(s):  
Omid Bagherieh ◽  
Roberto Horowitz
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Specific Class ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Trajectory Following

A specific class of track seeking problem has been considered in this paper, where the HDD head should follow a simple predetermined trajectory. This process is considered to be relatively slow, therefore, only VCM is used as an actuator. The accuracy of this trajectory following problem can be improved using iterative learning control, ILC. Two different ILC algorithms have been simulated and compared under existence of repeatable and non-repeatable disturbances.

High-performance tracking of high-speed supercavitating vehicles with uncertain parameters using novel parameter-optimal iterative learning control

Robotica ◽  
2014 ◽  
Vol 33 (08) ◽  
pp. 1653-1670 ◽  
Author(s):  
Meisam Yahyazadeh ◽  
Abolfazl Ranjbar Noei
Keyword(s):  
Phase Behavior ◽  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Minimum Phase ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Supercavitating Vehicle

SUMMARYThis paper proposes a new technique based on a Parameter-Optimal Iterative Learning Control (POILC) to track a command pitch rate of a high-speed supercavitating vehicle (HSSV). The pitch rate of a supercavitating vehicle has non-minimum phase behavior. Thus, tracking is fundamentally limited to poor performance. To solve this problem, a feed-forward control can be used while using the cavitator as a control input in the feed-forward path to modify the slow response caused by non-minimum phase behavior. The main idea of this paper is to apply the cavitator input with high precision as a feed-forward control to improve tracking performance. The exact value of the feed-forward control is achieved using POILC. However, in the presence of uncertainty, zero convergence of POILC algorithms is threatened. It will be shown that applying adaptive weight in the performance index, the convergence is guaranteed in the presence of uncertainty and also when the system is sign-indefinite. The proposed technique includes an optimal planning to make the error norm monotonically convergent to zero. The convergence and perfect tracking will be guaranteed through a Lyapunov candidate. Performance and significance of the proposed supercavitating vehicle control will be verified by simulation.

scholarly journals Chaotic Synchronizing Systems with Zero Time Delay and Free Couple via Iterative Learning Control

2017 ◽  
Author(s):  
Chun-Kai Cheng ◽  
Paul C.-P. Chao
Keyword(s):  
Time Delay ◽  
Iterative Learning Control ◽  
Mathematical Theory ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Control Input ◽  
Feed Forward Control ◽  
Response Systems ◽  
Error Dynamics

This research not only dedicated a less restrictive method of iteration-varying function for a learning control law to design a controller but also synchronize two nonlinear systems with free time-delay. In addition, the mathematical theory of system synchronization has proved rigorously and the theory verified through an example to demonstrate the behavior of each parameter in the theory. The design of a controller using the iterative learning control law is significant for robotic tracking. The controller in this research generates a feed-forward control input using the error dynamics among the drive-response systems. The error dynamics satisfies the Lyapunov function and the combination of output errors, which respectively represented relative estimated differences of the drive-response systems. The iterative learning control rule serves the function of a filter adding previous control error after the end of each iteration. The numerical example of a synchronous system is given a Lorenz system for driving and another with the iterative learning control law for response under different initial condition. The results verify and demonstrate the proposed mathematical theory. The simulation exhibits consistency in the behavior of each parameter to match mathematical theory.

Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

2018 ◽  
Vol 06 (03) ◽  
pp. 197-208 ◽  
Author(s):  
Gijo Sebastian ◽  
Ying Tan ◽  
Denny Oetomo ◽  
Iven Mareels
Keyword(s):  
Iterative Learning Control ◽  
Internal State ◽  
Tracking Error ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Feed Forward Control ◽  
Output Constraints ◽  
Rehabilitation Robotic

Motivated by the safety requirement of rehabilitation robotic systems for after stroke patients, this paper handles position or output constraints in robotic manipulators when the patients repeat the same task with the robot. In order to handle output constraints, if all state information is available, a state feedback controller can ensure that the output constraints are satisfied while iterative learning control (ILC) is used to learn the desired control input through iterations. By incorporating the feedback control using barrier Lyapunov function with feed-forward control (ILC) carefully, the convergence of the tracking error, the boundedness of the internal state, the boundedness of input signals can be guaranteed along with the satisfaction of the output constraints over iterations. The effectiveness of the proposed controller is demonstrated using simulations from the model of EMU, a rehabilitation robotic system.

Virtual Lash Adjuster for an Electromechanical Valve Actuator Through Iterative Learning Control

2003 ◽  
Author(s):  
Katherine S. Peterson ◽  
Anna G. Stefanopoulou ◽  
Yan Wang ◽  
Tom Megli
Keyword(s):  
Impact Velocity ◽  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Feed Forward ◽  
Valve Opening ◽  
The Impact

The reduction of impacts which occur in electromechanical valve actuators due to the presence of valve lash have been largely neglected in the literature. Instead, the majority of work in this area has focused on impacts occurring elsewhere. As such, a controller is presented here to account for the impacts which occur during the release phase of the valve opening due to the presence of valve lash. A combination of feed forward and iterative learning control are used to achieve trajectory tracking during the release bounding the impact velocity by 0.4 m/s.

HYBRID ITERATIVE LEARNING CONTROL FOR POSITION TRACKING OF AN ELECTRO HYDRAULIC SERVO SYSTEM

2019 ◽  
Vol XVI (2) ◽  
pp. 31-42
Author(s):  
Mansoor Zahoor Qadri ◽  
Ahsan Ali ◽  
Inam-ul-Hassan Sheikh
Keyword(s):  
Iterative Learning Control ◽  
Position Control ◽  
Servo System ◽  
Learning Control ◽  
Iterative Learning ◽  
Feed Forward ◽  
Percent Improvement ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Feed Forward Controller

Accurate position control of an electro hydraulic servo system (EHSS) is a challenging task due to inherent system nonlinearities, parametric variations and un-modelled dynamics. Since feedback controllers alone cannot provide perfect tracking control, an integration of feedback and feed forward controller is required. A cascaded iterative learning control (ILC) technique for position control of EHSS is proposed in this paper. ILC is a feed forward controller which modifies the reference signal for a feedback fractional order proportional-integral-derivative (PID) controller by learning through current control input and previous error obtained through trails. Unlike other feed forward controllers, ILC works on signal instead of system which eliminates the need of complete knowledge of the system. As compared to other controllers, the proposed technique provides fast convergence without the need of reconfiguring the existing control loop. Simulation and experiments revealed the effectiveness of the proposed technique for EHSS. The obtained results indicated eight percent improvement in rise time and nearly twenty one percent improvement in the settling time.

High-Precision Tracking of Piezoelectric Actuator Using Iterative Learning Control

2018 ◽  
Vol 06 (03) ◽  
pp. 175-183 ◽  
Author(s):  
Deqing Huang ◽  
Yupei Jian
Keyword(s):  
Iterative Learning Control ◽  
High Performance ◽  
Learning Control ◽  
Industrial Applications ◽  
Iterative Learning ◽  
Sampled Data ◽  
Feed Forward ◽  
Hysteresis Compensation ◽  
Data Feedback ◽  
Precision Tracking

Piezoelectric actuators (PEAs) have been widely used in industrial applications. In this paper, by assuming that the dynamic model of PEA takes a linear or Hammerstein structure, three iterative learning control (ILC) schemes are exploited to perform high-performance tracking control for PEA, including a sampled-data feed-forward ILC, a sampled-data feedback-based ILC and a sampled-data feed-forward ILC in combination with some hysteresis compensation techniques. The convergence conditions for each ILC algorithm have been developed. By selecting appropriate R-filter and learning filters, these three ILC algorithms have been implemented. The experimental results show the effectiveness of these three algorithms.

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