• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.69-69
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• 2000

Control systems using a hydraulic cylinder as an actuator are modeled to a nonlinear system owing to varying of moments and nonlinearities of hydraulic itself. In this paper, we want to control nonlinear hydraulic systems by adopting the fuzzy PID control technique which include nonlinear time varying control parameters. To do this, we propose the design method of fuzzy Pm controller and in order to assure effectiveness of fuzzy PID controller, computer simulations were executed for the control system.

• Smart Structures and Systems
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• v.11 no.3
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• pp.315-329
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• 2013

The construction of an experimental nonlinear structural model with little cost and unlimited repeatability for vibration control study represents a challenging task, especially for material nonlinearity. This paper reports the design, analysis and vibration control of a nonlinear structural experiment platform with adjustable hinges. In our approach, magnetorheological rotary brakes are substituted for the joints of a frame structure to simulate the nonlinear material behaviors of plastic hinges. For vibration control, a separate magnetorheological damper was employed to provide semi-active damping force to the nonlinear structure. A dynamic neural network was designed as a state observer to enable the feedback based semi-active vibration control. Based on the dynamic neural network observer, an adaptive fuzzy sliding mode based output control was developed for the magnetorheological damper to suppress the vibrations of the structure. The performance of the intelligent control algorithm was studied by subjecting the structure to shake table experiments. Experimental results show that the magnetorheological rotary brake can simulate the nonlinearity of the structural model with good repeatability. Moreover, different nonlinear behaviors can be achieved by controlling the input voltage of magnetorheological rotary damper. Different levels of nonlinearity in the vibration response of the structure can be achieved with the above adaptive fuzzy sliding mode control algorithm using a dynamic neural network observer.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.218-218
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• 2000

Advanced Vehicle Control Systems(AVCS) is one of the key elements in Intelligent Transportation Systems(ITS). This paper considers the problem of longitudinal control in vehicle platoon on a straight lane of a highway. In a very simplified situation, longitudinal vehicle dynamics contains many nonlinear elements. The nonlinear characteristics are mainly composed of an engine, a torque converter, and a drag force. In this paper, sliding control, one of nonlinear control methods, is applied to longitudinal automated vehicle control for platooning. Output feedback linearization is also simulated for comparison with the sliding control. Simulations for comparative study for the adopted controllers such as sliding control and output feedback linearization are peformed under the same conditions. This Paper aims at clarifying the characteristics of sliding control and output feedback linearization.

• 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 Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.958-961
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• 2001

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.76-82
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• 2009

A Heating, Ventilating and Air Conditioning (HVAC) system is a nonlinear multi-input multi-output (MIMO) system. This system is very difficult to control the temperature and the humidity ratio of a thermal space because of complex nonlinear characteristics. This paper proposes an adaptive fuzzy output feedback control based on observer for the nonlinear HVAC system. The nonlinear HVAC system is linearized through dynamic extension. State observers are designed for estimating state variables of the HVAC system. Fuzzy systems are employed to approximate uncertain nonlinear functions of the HVAC system with unavailable state variables. The obtained controller compares with an adaptive feedback controller. Simulation is given to demonstrate the effectiveness of our proposed adaptive fuzzy method.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.244-251
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• 2005

In this paper, we study the problem of stabilizing a general nonlinear control system by means of gradient descent control method which is a dynamic feedback control law. In this method, the general nonlinear control system can be considered as an affine nonlinear control systems. Then by using Sontag's formula we investigate the stability (asymptotic) of the general nonlinear control system.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.320-328
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• 2003

This paper proposes a new nonlinear adaptive guidance law. Fourth order state equation for integrated guidance and control loop is formulated considering target uncertainties and control loop dynamics. The state equation is further changed into the normal form by nonlinear coordinate transformation. An adaptive nonlinear guidance law is proposed to compensate for the uncertainties In both target acceleration and control loop dynamics. The proposed law adopts the sliding mode control approach with adaptation fer unknown bound of uncertainties. The present approach can effectively solve the existing guidance problem of target maneuver and the limited performance of control loop. We provide the stability analyses and demonstrate the effectiveness of our scheme through simulations.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.271-281
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• 2003

In this paper, a new nonlinear programming approach is suggested to solve biaffine matrix inequality (BMI) problems in multiobjective and structured controls. It is shown that these BMI problems are reduced to nonlinear minimization problems. An algorithm that is easily implemented with existing convex optimization codes is presented for the nonlinear minimization problem. The efficiency of the proposed algorithm is illustrated by numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.159-162
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• 1997

This paper describes robust stabilization of nonlinear single-input uncertain systems without matching conditions. We consider nonlinear systems with a vector of unknown constant parameters perturbed about a known value. The approach utilizes the generalized controller canonical form to lump the unmatched uncertainties recursively into the matched ones. This can be achieved via nonlinear coordinate transformations which depend not only on the states of the nonlinear system but also on the control input. Then the dynamic robust control law is derived and the stability result is also presented.