• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.329-336
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• 2009

In this paper, we propose the design of an optimized fuzzy cascade controller for rotary inverted pendulum system by means of Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) which is a kind of parallel genetic algorithms. The rotary inverted pendulum system is the system for controlling the inclination of pendulum axis through the adjustment of rotating arm. The control objective of the system is to control the position of rotating arm and to make the pendulum maintain the unstable equilibrium point of vertical position. To control rotary inverted pendulum system, we designs the fuzzy cascade controller scheme consisted of two fuzzy controllers and optimizes the parameters of the designed controller by means of HFCGA. A comparative analysis between the simulation and the practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller leads to superb performance in comparison with the conventional LQR controller as well as HFCGA based PD cascade controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2322-2329
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• 2008

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of particle swarm optimization(PSO) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling facrors) of each fuzzy controller using PSO. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on PSO, is presented in comparison with the conventional PD cascade controller based on serial genetic alogritms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.972-980
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• 2010

In this study, we introduce the design methodology of an optimized type-2 fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. The type-2 fuzzy cascade controller scheme consists of the outer controller and the inner controller as two cascaded fuzzy controllers. In type-2 fuzzy logic controller(FLC) as the expanded type of type-1 fuzzy logic controller(FLC), we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. The control parameters(scaling factors) of each fuzzy controller using HFCGA which is a kind of parallel genetic algorithms(PGAs). HFCGA helps alleviate the premature convergence being generated in conventional genetic algorithms(GAs). We estimated controller characteristic parameters of optimized type-2 fuzzy cascade controller applied ball & beam system such as maximum overshoot, delay time, rise time, settling time and steady-state error. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.391-398
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• 2009

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. The displacement change the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling factors) of each fuzzy controller using HFCGA. The inner controller controls the position of lever arm which corresponds to the position angle of a servo motor and the outer controller decides the set-point value of the inner controller. HFCGA is a kind of parallel genetic algorithms(PGAs), and helps alleviate the premature convergence being generated in conventional genetic algorithms (GAs). For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.286-286
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• 2000

We consider a negative unity feedback control system in which Che PIO, PI, PD or P controller and a transfer function having only poles are in cascade, We define the notion of the structural polynomial which means that there exists a subdomain of the coefficient space in which the polynomial is Hurwitz (left half plane stable) polynomial. We obtain the necessary and sufficient condition of the structure of the transfer function of which the characteristic polynomial is a structural polynomial, In addition, this paper present another necessary and sufficient condition for the existence of a constant gain controller with which the characteristic polynomial is structurally stable, For the structurally stabilizable P controller, it is allowed that the transfer function may not to all pole plants.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.308-309
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• 2007

본 논문에서는 계층적 공정 경쟁 기반 병렬 유전자 알고리즘 (Hierarchical Fair Competition Genetic Algorithm: HFCGA)을 이용하여 Ball & Beam 시스템에 최적의 Fuzzy Cascade 제어기를 설계하고자 한다. Ball & Beam 시스템은 비선형적이며 Beam의 마찰계수와 Ball의 중력 가속도를 고려하여 Ball의 위치를 조정하는 시스템이다. 이러한 Ball & Beam 시스템에 대해 Fuzzy Cascade 제어기를 설계하고, 조기 수렴에 문제가 있는 기존의 유전자 알고리즘을 개선한 HFCGA를 이용하여 제어기의 파라미터를 최적화 한다. 마지막으로 실제 플랜트에 적용하여 설계된 제어기의 성능을 평가하고, PD Cascade 제어기와 Fuzzy Cascade 제어기의 성능을 비교한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1462-1469
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• 1997

This study presents a genetic algorithm-based method for optimizing control parameters in fluid power systems. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics. A genetic algorithm seeks control parameters maximizing a measure that evaluates system performance. Five control gains of the PID-PD cascade controller fr an electrohydraulic speed control system with a variable displacement hydraulic motor are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that optimization of the five gains by manual tuning should be a task of great difficulty and that a genetic algorithm is an efficient scheme giving economy of time and in labor in optimizing control parameters of fluid power systems.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.48-55
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• 2004

This study presents a genetic algorithm-based method fur optimizing control parameters in the pressure control of electro-hydraulic pump with variable displacement. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics and search the optimal control parameters maximizing a measure that evaluates the performance of a system. Four control gains of the PI-PD cascade controller for an electro-hydraulic pressure control system are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that genetic algorithm is an efficient scheme in optimizing control parameters of the pressure control of electro-hydraulic pump with variable displacement.