• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1086-1089
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• 2012

We propose a near optimal controller design method for ramp metering based on SDRE (State Dependent Riccati Equation) approach. We parameterize the optimal nonlinear controller in terms of the solution matrices of an SDRE. We also give a simple algorithm to obtain the controller gain. Finally we give numerical results to show the effectiveness of the proposed near optimal traffic controller design method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.259-267
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• 2003

In this paper, we propose a nonlinear variable PID controller using a cell-mediated immune response. An immune feedback response is based on the functioning of biological T-cells. An immune feedback response and P-controller of conventional PID controllers resemble each other in role and mechanism. Therefore, we extend immune feedback mechanism to nonlinear PE controller. And in order to choose the optimal nonlinear PID controller games, we also propose the on-line tuning algorithm of nonlinear functions parameters in immune feedback mechanism. The trained parameters of nonlinear functions are adapted to the variations of the system parameters and any command velocity. And the adapted parameters obtained outputs of nonlinear functions with an optimal control performance. To verify performances of the proposed control systems, the speed control of nonlinear BC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system variations.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.3
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• pp.25-30
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• 2005

Nonlinear optimal control problems lead to Hamilton-Tacobi equations which are not analytically solvable for most practical problems. This difficulty has led to the development of suboptimal nonlinear design techniques such as controller design based on feedback linearization(FL). In this paper, we present some simple examples where the optimal answer can be found for the optimal controller, FL controller and linear controller and determine its relative performance. As a result, we get the condition of a nonlinear system for the FL controller to an optimal design.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.51-53
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• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.160-170
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• 2008

Temperature control of an enclosed thermal system which has many applications including Rapid Thermal Processing (RTP) of semiconductor wafers showed an input-constraint violation for nonlinear controllers due to inherent strong coupling between the elements [1]. In this paper, a constrained nonlinear optimal control design is developed, which accommodates input constraints using the linear algebraic equivalence of the nonlinear controllers, for the temperature control of an enclosed thermal process. First, it will be shown that design of nonlinear controllers is equivalent to solving a set of linear algebraic equations-the linear algebraic equivalence of nonlinear controllers (LAENC). Then an input-constrained nonlinear optimal controller is designed based on that LAENC using the constrained linear least squares method. Through numerical simulations, it is demonstrated that the proposed controller achieves the equivalent performances to the classical nonlinear controllers with less total energy consumption. Moreover, it generates the practical control solution, in other words, control solutions do not violate the input-constraints.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.68-73
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• 2000

This paper addresses the analysis and design of fuzzy control systems for a class of complex nonlinear systems. Firstly, the nonlinear system is represented by Takagi-Sugeno(TS) fuzzy model and the global controller is constructed by compensating each linear model in the rule of TS fuzzy model. The design of conventional TS fuzzy-model-based controller is composed of two processes. One is to determine the static state feedback gain of each local model and the other is to validate the stability of the designed fuzzy controller. In this paper, we propose an alternative methods for the design of TS fuzzy-model-based controller. The design scheme is based on the extension of conventional optimal control theory to the design of TS fuzzy-model-based controller. By using the proposed method, the design and stability analysis of the TS fuzzy model-based controller is reduced to the problem of finding the solution of a set of algebraic Riccati equations. And we use the recently developed interior point method to find the solution of AREs, where AREs are recast as the LMI formulation. A numerical simulation example is given to show the effectiveness and feasibiltiy of the proposed fuzzy controller design method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1392-1395
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• 1996

Nonlinear PID controllers have increasingly used in current industrial practice because it is robust and is easy to operate. Little guideline and tuning method, however, has been recommended for the nonlinear PID controllers while a lot of result is available for the linear PID controllers. Application guideline and tuning formulae are presented for error square type nonlinear controllers, which are most popular currently, are presented.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.510-512
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• 1999

In this paper, we present a method for the analysis and design of fuzzy controller for nonlinear systems. In the design procedure, we represent the dynamics of nonlinear systems using a Takagi-Sugeno fuzzy model and formulate the controller rules, which shares the same fuzzy sets with the fuzzy system, using parallel distributed compensation method. Then, after the feedback gain of each local state feedback controller is obtained using the existing optimal pole-placement scheme, we construct an overall fuzzy logic controller by blending all local state feedback controller. Finally, the effectiveness and feasibility of the proposed fuzzy-model-based controller design method has been evaluated through an inverted pendulum system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.3
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• pp.134-141
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• 2003

PID controllers with constant gains have been widely used in various control systems due to its powerful performance and easy implementation. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a nonlinear variable PR controller with immune feedback mechanism. An immune feedback mechanism is based on the functioning of biological T-cells, they include both an active term, which controls response speed. and an inhibitive term, which controls stabilization effect. Therefore, the proposed nonlinear PID controller is based on immune responses of biological. immune feedback mechanism which is the cell mediated immunity and In order to choose the optimal nonlinear PID controller games, we also propose the tuning algorithm of nonlinear function parameter in immune feedback mechanism. To verify performance of the proposed algorithm, the speed control of nonlinear DC motor are performed. Front the simulation results, we have found that the proposed algorithm is more superior to the conventional constant fain PID controller.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.393-403
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• 1997

In this paper, we proposed an optimal identification method of identifying the membership func¬tions and the fuzzy rules for the stabilization controller of the nonlinear system by RVEGA( Real Variable Elitist Genetic Algo rithm l. Although fuzzy logic controllers have been successfully applied to industrial plants, most of them have been relied heavily on expert's empirical knowl¬edge. So it is very difficult to determine the linguistic state space partitions and parameters of the membership functions and to extract the control rules. Most of conventional approaches have the drastic defects of trapping to a local minima. However, the proposed RVEGA which is similiar to the processes of natural evolution can optimize simulta¬neously the fuzzy rules and the parameters of membership functions. The validity of the RVEGA - based fuzzy controller was proved through applications to the stabi¬lization problems of an inverted pendulum system with highly nonlinear dynamics. The proposed RVEGA - based fuzzy controller has a swing -. up control mode(swing - up controller) and a stabi¬lization one(stabilization controller), moves a pendulum in an initial stable equilibrium point and a cart in an arbitrary position, to an unstable equilibrium point and a center of the rail. The stabi¬lization controller is composed of a hierarchical fuzzy inference structure; that is, the lower level inference for the virtual equilibrium point and the higher level one for position control of the cart according to the firstly inferred virtual equilibrium point. The experimental apparatus was imple¬mented by a DT -- 2801 board with AID, D/A converters and a PC - 586 microprocessor.