• Proceedings of the KIEE Conference
• /
• 1989.07a
• /
• pp.112-115
• /
• 1989

This paper proposes a learning control algorithm for trajectory tracking of large scale system. The controller using only localized informations is composed of stabilizing controller and iterative learning controller. Stabilization and convergence of each subsystem is assured under some conditions which are inequalities of inter-connection terms and learning controller gain.

• Proceedings of the KIEE Conference
• /
• 1989.11a
• /
• pp.367-371
• /
• 1989

As a menas of designing a robust servo system for electrical motor drive system, an iterative learning control method is proposed by employing the structure of the model algorithmic control. A sufficient condition for the convergency is shown, and via simulation for permanent magnet synchronous motor drive system, it is demonstrated 1hat the method yields a 'good performance even in the presence of the external load distrurbances.

• Journal of Advanced Marine Engineering and Technology
• /
• v.38 no.10
• /
• pp.1281-1286
• /
• 2014

This paper presents an iterative learning control (ILC) approach for tracking problems with specified data points that are desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, an algorithm which updates not only the control signal but also the reference trajectory at each trial will be developed. The relationship between the reference trajectory and ILC control in tracking problems where there are specified data points through which the system should pass is investigated as the rate of convergence. In traditional ILC, the desired data is stored in a tracking profile file. Due to the huge size of the data file containing the target points, it is important to reduce the computational cost. Finally, simulation results of the presented technique are mentioned and compared to other related works to confirm the effectiveness of proposed scheme.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.5
• /
• pp.395-401
• /
• 2004

We present an AILC(Adaptive Iterative Learning Control) scheme and a sufficient condition for system parameter identification for uncertain robotic systems that perform the same tasks repetitively. It is guaranteed that the joint velocity and position asymptotically converge to the reference joint velocity and position, respectively. In addition, it is proved that a sufficient condition for parameter identification is the PE(Persistent Excitation) condition on the regressor matrix evaluated at the reference trajectory during the operation period. Since the regressor matrix on the reference trajectory can be easily computed prior to the real robot operation, the proposed algorithm provides a useful method to verify whether the parameter error converges to zero or not.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.2
• /
• pp.204-210
• /
• 2018

As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.1
• /
• pp.117-120
• /
• 2008

This paper investigates the convergence condition of ADILC(iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.225-227
• /
• 2006

This note investigates the convergence condition of ADILC (iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• Korean Chemical Engineering Research
• /
• v.44 no.5
• /
• pp.425-434
• /
• 2006

The operability and productivity of continuous processes, especially in petrochemical industries have made remarkable improvement during the past twenty years through advanced process control (APC) typified by model-based predictive control. On the other hand, APC have not been actively practiced in industrial batch processes typified by batch polymerization reactors. Perhaps the main cause for this has been the lack of reliable batch process APC techniques that can overcome the unique problems in industrial batch processes. Recently, some noteworthy progress is being made in this area. New high-performance batch process control techniques that can accommodate and also overcome the unique problems of industrial batch processes have been proposed on the basis of iterative learning control (ILC). In this review paper, recent advancement in the batch process APC techniques are presented, with a particular focus on the variations of the so called Q-ILC method, with the hope that they are widely practiced in different industrial batch processes and enhance their operations.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.2
• /
• pp.175-179
• /
• 2003

Convergence condition determines performance of iterative learning control (ILC), for example, convergence speed, remaining error, etc. Hence, the performance can be elevated and a feasible set of learning controllers grows if a less conservative condition is obtained. In the frequency domain, the $H_{\infty}$ norm of the transfer function between consecutive errors has been currently used to test convergence of a learning system. However, even if the convergence condition based on the $H_{\infty}$ norm has a clear property about monotonic convergence, it has a few drawbacks, especially in MIMO plants. In this paper, the relation between the condition and the monotonicity of convergence is clarified and a modified convergence condition is found out using a frequency domain Lyapunov equation, which supersedes the conventional one in the frequency domain.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.53-68
• /
• 2005