• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.55-62
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• 2005

It is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic controller allows a tentative linguistic policy to be used as an initial rule base. At the design stage, if one can reasonably assemble a good collection of rules, it may then be possible to be tuned to improve the controller performance. In this paper, we proposed the shifting method of rule base table to improve the performance of fuzzy controller. The proposed method is based on the principle of that the effect of the output to regulate the system would be greater when the error increases and the effect of output would be less when the error decreases. According to simulation results, it is an effective method to improve the fuzzy control rule base and the performance of fuzzy logic controllers.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.24-32
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• 2003

The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.775-777
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• 1998

The Ziegler-Nichols parameter tuning has been widely known as a fairly heuristic method to good determine setting of PID controllers, for a wide range of common industrial processes. We extract process knowledge required for rule base controller through tuning experiment and simulation study, such as set point weighting and normalised gain and dead time of process. In this paper, we presents a rule base PID controller by extracted process knowledge and the modified Ziegler-Nichols tuning. Computer simulation are provided demonstrate the feasibility of this approach.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1022-1027
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• 1991

A new method to control a robot manipulator by neural networks is proposed. The controller is composed of both a PD controller and a neural network-based feedforward controller. MLP(multi-layer perceptron) neural network is used for the feedforward controller and trained by BP(back-propagation) learning rule. Error terms for BP learning rule are composed of the outputs of a PD controller and the acceleration errors of manipulator joints. We compare the proposed method with existing ones and contrast performances of them by simulation. Also, We discuss the real application of the proposed method in consideration of the learning time of the neural network and the time required for sensing the joint acceleration.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.4
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• pp.448-454
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• 2007

The new tuning rules of the PID controller for the rejection of load disturbance are proposed incorporating with real-coded genetic algorithms (RCGAs). The optimal gain parameters of the PID controller for a first-order plus time delay model are obtained based on a RCGA. Then tuning formula are derived using the tuned parameters sets potential tuning rule models and another RCGA. The performance criteria of the controller are adopted as ISE, IAE and ITAE. A series of simulation are carried out to verify the effectiveness of the proposed tuning rules.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.699-701
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• 2004

This paper proposes the algorithm of servo- writing based on feedback error learning neural networks. The controller consists of feedback controller using PID and feedforward controller using gaussian radial basis function network. Because the RBFNs are trained by on-line rule, the controller has adaptation capability. The performance of the proposed controller is compared to that of conventional PID controller. Proposed algorithm shows better performance than PID controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.162-165
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• 1996

In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.413-417
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• 1989

The idea of expert control is to incorporate a rule based expert system in a feedback control system. In this paper, the expert system concepts are instead used as an element of the feedback loop in a single controller. The algorithms are coded in as pure form as possible and the heuristic logic is implemented as rules. This paper reports on effort to produce an implementation of an expert controller on microcomputer based system, including an industrial programmable controller.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.948-955
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• 2008

The conventional PID controller has been widely employed in industry. However, the PID controller with one degree of freedom(DOF) can not optimize both set-point tracking response and disturbance rejection response at the same time. In order to solve this problem, a few types of 2DOF PID controllers have been suggested. In this paper, a tuning formula for a 2DOF PID controller is presented. The optimal parameter sets of the 2DOF PID controller are determined based on the first-order plus time delay process and a real-coded genetic algorithm(RCGA) such that the ITAE performance criterion is minimized. The tuning rule is then addressed using calculated parameter sets and another RCGA. A set of simulation works are carried out on three processes with time delay to verify the effectiveness of the proposed rule.