• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.383-385
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• 1997

Back-propagation is one of the efficient algorithms used to nonlinear optimizations or controls. In spite of its structual simplicity and learning ability, learning time is very long or bad case converge local minimum on complicate input patterns. In order to improve these matters varing learning rate and momentums were proposed. In this paper, to improve its performance fuzzy is adjusted in parameters, learning rate and momentums. Parameters are adjusted by errors and change of errors adaptively. In order to evaluate proposed method simulated with MATLAB on inverted pendulum.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.156-165
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• 1998

This paper proposes a systematic design methodology for the Takagi-Sugeno(TS) model based fuzzy control system with guaranteed stability and additional constraints on the closed-loop pole location. These combined two objectives are formulated as a system of LMIs(Linear Matrix Inequalities). Since LMIs intrinsically reflect constraints, they tend to offer more flexibility for combining various constraints on the closed-loop system. To demonstrate the usefulness of the proposed design methodology it is applied to the requlation problem of a nonlinear magnetic bearing system. Simulation results show that the proposed LMI-based design methodology yields not only maximized stability boundary but also the desired transient responses.

• Advances in aircraft and spacecraft science
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• v.9 no.2
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• pp.149-167
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• 2022

A Modified fuzzy mechanical control of large-scale multiple time delayed dynamic systems in states is considered in this paper. To do this, at the first level, a two-step strategy is proposed to divide a large system into several interconnected subsystems. And we focus on the damage propagation for aircraft structural analysis of composite materials. As a modified fuzzy control command, the next was received as feedback theory based on the energetic function and the LMI optimal stability criteria which allow researchers to solve this problem and have the whole system in asymptotically stability. And we focus on the results which shows the high effective by the proposed theory utilized for damage propagation for aircraft structural analysis of composite materials.

• Steel and Composite Structures
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• v.47 no.4
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• pp.513-521
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• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.501-503
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• 1999

In this paper, the design method of a hybrid fuzzy controller with an optimal auto-tuning method is proposed. The conventional PID controller becomes so sensitive to the control environments and the change of parameters that the efficiency of its utility for the complex and nonlinear plant has been questioned in transient state. In this paper, first, a hybrid fuzzy logic controller(HFLC) is proposed. The control input of the system in the HFLC is a convex combination by a fuzzy variable of the FLC's output in transient state and the PID's output in steady state. Second, a powerful auto-tuning algorithm is presented to automatically improve the Performance of controller, utilizing the improved complex method and the genetic algorithm. The algorithm estimates automatically the optimal values of scaling factors and PID coefficients. Controllers are applied to the plants with time-delay and the DC servo motor Computer simulations are conducted at the step input and the system performances are evaluated in the ITAE.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1668-1669
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• 2007

In this paper, we propose nonlinear congestion controller for active queue management of differentiated-services networks. Two important issues in differentiated-services architecture are bandwidth guarantee and fair sharing of unsubscribed bandwidth among TCP flows with and without bandwidth reservation. The nonlinear congestion controller was composed fuzzy logic controller and state feedback controller. The nonlinear congestion controller methodology has been applied to a TCP network. We use NS-2 simulation to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.109-115
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• 2010

A main technology of opening and closing a sluice gate is accurate synchronous and position control for the two cylinders when they are moving with the sluice gate together over 10[m]. Since the supply flow and supply pressure of cylinders are not constant and a nonlinear friction force of the piston in cylinders exists, a difference will be made between the displacement of two cylinders. This difference causes the sluice gate to deform and abrade, and even it may be out of order. In order to solve this problem we design two kinds of fuzzy PI controllers. The former is for a position control of two cylinders, the latter is for their synchronous control. We show some simulation results compare the performance of fuzzy PI controller to the conventional PID controller.