• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.5
• /
• pp.74-83
• /
• 1996

An iterative tuning technique is derived for PID controllers which are widely used in industries. The tuning algorithm is based upon a fuzzy indirect reasoning method and an iterative technique. The PID gains for the first tuning action are determined by a method which is modified from the Ziegler-Nichols step response method. The first PID gains are determined to obtain a control performance so close to a design performance that the following tuning process can be made effectively. The design paramaters are given as time-domain variables which human is familiar with. The results of simulation studies show that the proposed tuning method can produce an effective tuning for arbitrary design performances.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.303-306
• /
• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.640-645
• /
• 2005

Closed-loop model-free optimization of positive feedback time delay controllers for dominant time delay systems is presented. Iterative feedback tuning (IFT) is applied to the tuning of positive feedback time delay controller. Three experiments are carried out to perform the model-free gradient descent optimization. The initial controller parameters and duration in specifying the cost function are suggested. The effects of step size, filter function and time weighting function on the performance of the optimized controlled are given. Simulation and experimental studies are included to demonstrate the effectiveness of the tuning scheme.

• Journal of Aerospace System Engineering
• /
• v.11 no.5
• /
• pp.28-35
• /
• 2017

Magnetic Suspension and Balance System (MSBS) demonstrates the capacity to levitate an experimental model absent any mechanical contact using magnetic forces and moments. It allows precise control of position and attitude of the model, and measures external forces and moments acting on the model. For the purpose of acquisition of reliable experimental results under stable and safe conditions, the performance and robustness of the position and attitude control system of MSBS needs to be improved. To this end, Iterative Feedback Tuning (IFT) and L1 adaptive output feedback algorithm were employed to automatically increase command following performance and to ensure robust operation of MSBS with failure of electric power supply. The applicability was validated using computational simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1992.10a
• /
• pp.182-186
• /
• 1992

A self tuning technique is derived for PID controllers which are widely used in industries. The tuning algorithm is based upon a fuzzy indirect reasoning method and an iterative technique. The fuzzy technique is considered to obtain ease and simplicity of tuning process. The PID gains for the first tuning action are determined by a method which is modified from the Ziegler-Nichols step response method. The first PID gains are determined to obtain a control performance so close to a design performance that the followed tuning process can be made effectively. The design parameters are given as time-domain variables which human is familiar with. The results of simulation studies show that the proped tuning method can produce an effective tuning for arbitaray design performances.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.10
• /
• pp.888-893
• /
• 2000

This paper deals with the development of an automatic design software for DC servo motor control, which provides good performance with rapid response and velocity control accuracy. In the proposed method, the design is automatically executed using Matlab, and iterative learning control algorithms are used in the design process. We applied this method to 50W, 100W, 200W, 300W, 500W, 750W, 1.8kW and 4.5kW DC servo motors which are widely used in the industry. We compare the results of the manual tuning design method with that of the automatic design method presented in this paper. From the experimental results, we can find that the performance of the proposed method is better than that of the manual tuning design method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.7
• /
• pp.683-687
• /
• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• ETRI Journal
• /
• v.40 no.4
• /
• pp.421-434
• /
• 2018

The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low-end smart devices that are widely used in CPSs. In this paper, we aim to determine whether DNNs can be efficiently deployed and operated in low-end smart devices. To do this, we develop a method to reduce the memory requirement of DNNs and increase the inference speed, while maintaining the performance (for example, accuracy) close to the original level. The parameters of DNNs are decomposed using a hybrid of canonical polyadic-singular value decomposition, approximated using a tensor power method, and fine-tuned by performing iterative one-shot hybrid fine-tuning to recover from a decreased accuracy. In this study, we evaluate our method on frequently used networks. We also present results from extensive experiments on the effects of several fine-tuning methods, the importance of iterative fine-tuning, and decomposition techniques. We demonstrate the effectiveness of the proposed method by deploying compressed networks in smartphones.

• The Journal of Korea Robotics Society
• /
• v.17 no.2
• /
• pp.221-229
• /
• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• Structural Engineering and Mechanics
• /
• v.76 no.2
• /
• pp.163-173
• /
• 2020

As a vehicle moves on multiple equal-span beams at constant speed, the running vehicle would be subjected to repetitive excitations from the beam vibrations under it. Once the exciting frequency caused by the vibrating beams coincides with any of the vehicle's frequencies, resonance would take place on the vehicle. A similar resonance phenomenon occurs on a beam subject to sequential moving loads with identical axle-intervals. To reduce both resonant phenomena of a vehicle moving on guideway girders, this study proposed an additional feedback controller based the condensed virtual dynamic absorber (C-VDA) scheme. This condensation scheme has the following advantages: (1) the feedback tuning gains required to adapt the control currents or voltages are directly obtained from the tuning forces of the VDA; (2) the condensed VDA scheme does not need additional DoFs of the absorber to control the vibration of the maglev-vehicle/guideway system. By decomposing the maglev vehicle-guideway coupling system into two sub-systems (the moving vehicle and the supporting girders), an incremental-iterative procedure associated with the Newmark method is presented to solve the two sets of sub-system equations. From the present studies, the proposed C-VDA scheme is a feasible approach to suppress the interaction response for a maglev vehicle in resonance moving on a series of guideway girders.