• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.9
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• pp.922-929
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• 2009

This paper deals with the State-Dependent Riccati Equation (SDRE) Technique for the design of helicopter nonlinear waypoint guidance controller. To generate the flight guidance through multiple waypoints, we use the trigonometric spline. The controller design and its validation is based upon a level 2 simulation helicopter model and the designed SDRE controller is applied to the trajectory tracking problems. To validate the designed SDRE controller, the simulation environment of high fidelity helicopter model is developed using three independent computers. This paper focuses on the validation the present SDRE controller through the helicopter waypoint guidance simulation.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1577-1583
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• 2015

In this research, a combined adaptive-robust current controller is developed for non-minimum-phase DC-DC converters in a wide range of operations. In the proposed nonlinear controller, load resistance, input voltage and zero interval of the inductor current are estimated using developed adaptation rules and knowing the operating mode of the converter for the closed-loop control is not required; hence, a single controller can be employed for a wide load and line changes in discontinuous and continuous conduction operations. Using the TMS320F2810 digital signal processor, the experimental response of the proposed controller is presented in different operating points of the buck/boost converter. During transition between different modes of the converter, the developed controller has a better dynamic response compared with previously reported adaptive nonlinear approach. Moreover, output voltage steady-state error is zero in different conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.139-144
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• 2011

We consider the stabilization problem for a class of networked control systems with random delays in the discrete-time domain. The controller-to-actuator and sensor-to-controller time-delays are modeled as two Markov chains, and the resulting closed-loop systems are Markovian jump nonlinear systems with two modes. The T-S (Takagi-Sugeno) fuzzy model is employed to represent a nonlinear system with Markovian jump parameters. The aim is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. The necessary and sufficient conditions on the existence of stabilizing fuzzy controllers are established in terms of LMIs (Linear Matrix Inequalities). It is shown that fuzzy controller gains are mode-dependent. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.127-134
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• 2005

This paper proposes a nonlinear fuzzy PID controller with variable parameters to improve slow rising time and divergence occurred by limited input spaces and a resultant limited control input during fuzzification in a fuzzy PID controller with fixed parameters, and describes the design principle and tracking performance of a proposed fuzzy PID controller. The parameters of a proposed controller are adjusted by the stability conditions derived from 'small gain theorem' and satisfy the BIBO stability of overall control system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.75.5-75
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• 2001

In this paper, we study design of a ILQ(Inverse Linear Quadratic optimal control) looper control system for hot strip mills. The looper which is placed between stands plays an important role in controlling strip width by regulating strip tension variation generated from the velocity difference of main work rolls. A Looper servo controller is designed by ILQ control theory which is an inverse problem of LQ(Linear Quadratic optimal control) control. The mathematical model for looper system is obtained by Taylor´s linearization of nonlinear differential equations. Then we designed linear controller for linearization model by using the ILQ control algorithm. Thereafter this controller is applied to the nonlinear model for model identification. As a result, we show the controller´s robustness for the model error, external disturbance and sensor noise.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.1
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• pp.12-18
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• 2008

This paper describes two run-to-run controllers, a nonlinear multiple exponential-weight moving-average (NMEWMA) controller and a dynamic model-tuning minimum-variance (DMTMV) controller, for photolithography processes. The relationships between the input recipes (exposure dose and focus) and output variables (critical dimensions) were formed using an experimental design method, and the photolithography process model was built using a multiple regression analysis. Both the NMEWMA and DMTMV controllers could update the process model and obtain the optimal recipes for the next run. Quantified improvements were obtained from simulations and real photolithography processes.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.25-32
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• 1994

A nonlinear control algorithm for the depth control of underwater vehicles is presented. In order to consider the deadzone effect of the flow control valve, a nonlinear fuzzy logic controller (FLC) is synthesized and combined with a linear proportional-derivative-acceleration (PDA) controller, which is called the PDA/FLC controller. And to show the effectiveness of the PDA/FLC control system, it is compared with the linear PDA control system through computer simulation. It is found that the PDA/FLC control system is suitable one to maintain the desirable depth of underwater vehicles with deadzone.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.23-26
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• 2000

Generally, the wheel-driven mobile robot systems, by their structural property, have nonholonomic constraints. These constraints are not integrable and cannot be written as time derivatives of some functions with respect to the generalized coordinates. Hence, nonlinear approaches are required to solve the problems. In this paper, the trajectory controller of wheeled mobile robot systems is suggested to guarantee its convergence to reference trajectory. Design procedure of the suggested trajectory controller is back-stepping scheme which was introduced recently in nonlinear control theory. The performance of the proposed trajectory controller is verified via computer simulation. In the simulation, the trajectory controller is applied to differentially driven robot system and car-like mobile robot system on the assumption that the trajectory planner be given.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.58-65
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• 2003

In this paper, a new model, which is a Takagi-Sugeno fuzzy model, for mobile robot is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and the outer loop is a PI controller designed for tracking the reference input, is suggested. Because the robot dynamics is nonlinear, it requires the controller to be insensitive to the nonlinear term. To achieve this objective, the model is developed by well known T-S fuzzy model. The design algorithm of inner state-feedback loop is regional pole-placement. In this paper, regions, for which poles of the inner state feedback loop are lie in, are formulated by LMI's. By solving these LMI's, we can obtain the state feedback gains for T-S fuzzy system. And this paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ(linear quadratic) cost. By using these properties, it is also shown in this paper that the PI controller can be obtained by solving the LQ problem.