• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.186-192
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• 2009

PID controllers are simple in structure and easy for implementation. However, they may produce large overshoots and over-oscillatory responses. Combining PID control with other control techniques often results in advanced hybrid schemes that are able to improve pure PID controllers. This paper proposes hybrid controller for position control system of servo motor. The proposed controller is composed of a subcontroller and a parallel PID controller. The subcontroller improves the transient system performance while the PID controller is mainly responsible for the steady-state system performance. A very promising advantage of this hybrid scheme, in terms of controller synthesis, is that the subcontrollers and controller components can be designed separately. Systematic design methods for various controller components are developed. The proposed hybrid scheme is applied to a DC motor position servo system. The effectiveness of the proposed controller is verified through the computer simulation results.

• Structural Engineering and Mechanics
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• v.36 no.3
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• pp.381-399
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• 2010

A fuzzy hybrid control technique using a semi-active tuned mass damper (STMD) has been proposed in this study for mitigation of wind induced motion of a tall building. For numerical simulation, a third generation benchmark is employed for a wind-excited 76-story building. A magnetorheological (MR) damper is used to compose an STMD. The proposed control technique employs a hierarchical structure consisting of two lower-level semi-active controllers (sub-controllers) and a higher-level fuzzy hybrid controller. Skyhook and groundhook control algorithms are used as sub-controllers. When a wind load is applied to the benchmark building, each sub-controller provides different control commands for the STMD. These control commands are appropriately combined by the fuzzy hybrid controller during realtime control. Results from numerical simulations demonstrate that the proposed fuzzy hybrid control technique can effectively reduce the STMD motion as well as building responses compared to the conventional hybrid controller. In addition, it is shown that the control performance of the STMD is superior to that of the sample TMD and comparable to an active TMD, but with a significant reduction in power consumption.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.599-609
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.24-29
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• 1990

In this paper ,ie propose a hybrid position/force controller of a robot manipulator using double-layer neural network. Each layer is constructed from inverse dynamics and Jacobian transpose matrix, respectively. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using a PUMA 560 manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.276-280
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• 1992

In the paper, an optimal tuning algorithm is presented to automatically improve the performance of a hybrid controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of a hybrid controller, according to the change rate and limitation condition of output, The controller is applied to plants with time-delay. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.85-89
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• 2004

Robust control for feedback control system is needed according to the highest precision of industrial automation. However, when a neural network feedback control system has an effect of disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, hybrid control method of neural network controller and PID controller is presented. A neural network controller is operated as a main controller, a PID controller is a assistant controller which operates only when some undesirable phenomena occur, e.q., when the error hit the boundary of constraint set. The robust control function of neural network-PID hybrid controller is demonstrated by speed control of Motor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.538-541
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• 1995

Joint flexibilities and frictional uncertainties are known to be a major cause of performance degration in motion control systems. This paper investigates the modeling and compensation of these undesired effects. A hybrid controller, which consists of a predictive controller and a neural network controller, is designed to overcome these undesired effects. Also learning scheme for friction uncertainies, which don't interfere with feedback controller dynamics, is discussed. Through simulation works with two inetia-torsional spring system having Coulomb friction, the effectiveness of the proposed hybrid controller was tested. The proposed predictive & neural network hybrid controller shows better performance over one when only predictive controller used.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.48-55
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• 2008

In this paper, we propose a hybrid fuzzy controller for high performance. Hybrid fuzzy controller are combined Fuzzy and PID controller. In tuning the controller, the parameters of PID and the factors fuzzy controllers were obtained from the model identification and by using genetic algorithms, respectively. Simulation examples demonstrated a better performance of the proposed controller than conventional ones.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2089-2091
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• 2001

In this paper, a hybrid controller that consists of a conventional PD controller and a neural network controller which adapts to various control conditions by online learning is used and a new learning algorithm of the neural networks is used to prevent weights of neural network from diverging. A conventional PI controller and the hybrid controller is applied to speed control of 3 phase induction motor. So in comparison with a PD controller, we prove superiority of hybrid controller by experiments.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.174-181
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• 1997

A proposed hybrid self-tuning control scheme for single rod hydraulic cylinder which has varying loads is presented here. An adaptive controller is developed for the system that use feedforward and P feedback control for simultaneous parameter identification and tracking control. Through experimental results, the performance comparison of the hybrid self-tuning controller with a constant gain P contro- ller clearly shows its superior ability in handling load changes in quiescent states.