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.

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.

Iterative learning control of air pressure variation inside a high-speed train under the excitation of the tunnel pressure wave with a fixed-morphologic form

2021 ◽  
pp. 095440972110327
Author(s):  
Zhiying He ◽  
Chunjun Chen ◽  
Dongwei Wang ◽  
Chao Deng ◽  
Jia Hu ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Pressure Wave ◽  
High Speed ◽  
Learning Control ◽  
Pressure Variation ◽  
Iterative Learning ◽  
Air Pressure ◽  
High Speed Train ◽  
Comfort Index ◽  
Simulation Signal

Based on the characteristics that the tunnel pressure wave has a fixed-morphologic form when the same train passes through the same tunnel, an applicational approach based on the iterative learning control (ILC) is developed, aiming at overcoming the drawbacks of the traditional strategy for controlling the air pressure variation inside a high-speed train carriage. To achieve the goal, the control system is mathematically modelled. Then, the problem is formulated. The task of suppressing the influence of the tunnel pressure wave on the air pressure inside the carriages is shifted as an ILC problem of tracking the comfort index with varying trial length. The algorithm of refreshing the control signal from trial to trial is determined and the process of ILC control is designed. Next, the convergence of the newly-developed applicational ILC algorithm is discussed and the algorithm is simulated by the simulation signal and field-test signal. Results show that the applicational ILC algorithm be more adaptable in handling the control of the air pressure inside carriage under the excitation of varying-amplitude, varying-scale and varying-initial-states tunnel pressure wave. Meanwhile, the matching with tunnel pressure wave makes the applicational ILC algorithm will take both the riding comfort and fresh air into consideration, which upgrades the performances when the high-speed train passing through long tunnels.

scholarly journals High-Speed Elevator Car Air Pressure Compensation Method Based on Coupling Analysis of Internal and External Flow Fields

2021 ◽  
Vol 11 (4) ◽  
pp. 1700
Author(s):  
Lemiao Qiu ◽  
Huifang Zhou ◽  
Zili Wang ◽  
Shuyou Zhang ◽  
Lichun Zhang ◽  
...  
Keyword(s):  
Pressure Fluctuation ◽  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Flow Fields ◽  
External Flow ◽  
Iterative Learning ◽  
Air Pressure ◽  
Coupling Analysis ◽  
Pressure Compensation

As the demand for high-speed elevators grows, the requirements of elevator performance have also developed. The high speed will produce strong airflow disturbances and drastic pressure changes, which is prone to cause passenger discomfort. In this paper, an elevator car air pressure compensation method based on coupling analysis of internal and external flow fields (IE-FF) is proposed. It helps to adaptively track the ideal air pressure curve (IAPC) inside the car and controls the air pressure fluctuation to improve the ride comfort of the elevator. To obtain the air pressure transient value in the elevator car, an IE-FF modeling method is proposed. Based on the IE-FF model, the air pressure compensation system is developed. To realize the air pressure compensation inside the car, an adaptive iterative learning control (A-ILC) algorithm is proposed, to eliminate the passengers’ ear pressing due to the severe air pressure fluctuation. To verify the proposed method, the KLK2 (Canny Elevator Co., Ltd., 2015, Suzhou, China) high-speed elevator is applied. The numerical experiment results show that the proposed method has higher tracking accuracy and convergence speed compared to the classical Proportion Integral Differential (PID) algorithm and the Proportion Integral-iterative learning control (PD-ILC) algorithm.

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.

Iterative Learning Control for a Class of Modeling Undesirable Single Degree of Freedom System

2014 ◽  
Vol 538 ◽  
pp. 379-382
Author(s):  
Wei Zhou ◽  
Bao Bin Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Degree Of Freedom ◽  
Control Input ◽  
Convergence Properties ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Simulation Results

A class of modeling undesirable single degree of freedom system is studied by using iterative learning control. The proposed iterative learning algorithm constantly updates the control input according to output error until the desired output occurred. So the system with designed controller can achieve perfect accuracy. We have proved convergence properties in iteration domain and simulation results demonstrate the effectiveness of the presented method.

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.

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