• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2372-2377
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,{\dot{x}},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.969-976
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• 2008

A position control problem is studied for UEV(Unmanned Expedition Vehicles), which is to follow pre-determined paths via fixed way-points. Hybrid control systems are used for position control of UEV depending on the operating condition. Speed control consists of three controllers: PID control, adaptive PI control, and neural network. Heading control consists of two controllers, PID and adaptive PID control. The controllers are selected based on the changes of road conditions. We suggest an adaptive PI control algorithm for speed control and an transition management algorithm among the controllers. The algorithm adapts the road conditions and variation of vehicle dynamical characteristics and selects a suitable controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.993-996
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• 2000

A brushless linear motor is suitable for a high-accuracy servo mechanism. It is also suitable for operation with higher speed and precision. Since it does not involve some sort of mechanical coupling, linear driving force can be applied directly. Basic models including magetomotive force and electromotive forces are introduced and simplified. Both conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.973-976
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• 2000

A brushless linear motor is suitalbe fur a high-accuracy servo mechanism. It is also suitable for operation with higher speed and precision. Since it does not involve some sort of mechanical coupling, linear driving force can be applied directly. Basic models including magetomotive farce and electromotive forces are introduced and simplified. Both conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.146-156
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,\dot{x},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.299-303
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• 2000

A brushless linear motor is suitable for operation with higher speed and precision. Since it does not involve mechanical coupling, linear driving force can be applied directly. Conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.129-134
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• 1992

Fuzzy theory has shown good control performance for non-linear system that is difficult to be controlled by the conventional controller. Backpropagation neural network can interpolate output without the priori knowledge of its dynamics. In this paper, we proposes a Fuzzy-Neural Controller. The Fuzzy Control by deterministic rule may not be sensitive for uncertain conditions and has a disadvantage of setting the rule by repeatedly experience. To solve such problems, we construct Self organizing Fuzzy-Neural Controller which can reorganize the fuzzy rule according to the state of system. Experimental results show that proposed Fuzzy-Neural Controller has better performance than conventional controller(PID) has especially rising time and overshoot characteristics.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.671-676
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• 2007

In this paper, a novel neuro-fuzzy controller is presented. The generic fuzzy controller is compensated by a neural network controller so that an overall control structure forms a neuro-fuzzy controller. The proposed neuro-fuzzy controller solves the difficulty of selecting optimal fuzzy rules by providing the similar effect of modifying fuzzy rules simply by changing crisp input values. The performance of the proposed controller is tested by controlling humanoid robot arms. The humanoid robot arm is analyzed and implemented. Experimental studies have shown that the performance of the proposed controller is better than that of a PID controller and of a generic fuzzy PD controller.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.497-499
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• 1998

the satellite tracking problem of Antenna with two axis of elevation angle and azimuth one is described in this paper. The proposed control procedures for stabilization of nonlinear pedestal unit are consists of a off-line modeling identified by neural network and a on-line neural network controller combined with a reference model using the least square method. the simulation results are introduced and compared to a conventional PID controller.