• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.268-274
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• 2013

Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.

• Structural Engineering and Mechanics
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• v.85 no.5
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• pp.607-620
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• 2023

The classical optimal control (COC) method has been widely used for linear quadratic regulator (LQR) problems of structural control. However, the equation of motion of the structure is incorporated into the optimization model as the constraint condition for the LQR problem, which needs to be solved through the Riccati equation under certain assumptions. In this study, an explicit optimal control (EOC) method is proposed based on the explicit time-domain method (ETDM). By use of the explicit formulation of structural responses, the LQR problem with the constraint of equation of motion can be transformed into an unconstrained optimization problem, and therefore the control law can be derived directly without solving the Riccati equation. To further optimize the weighting parameters adopted in the control law using the gradient-based optimization algorithm, the sensitivities of structural responses and control forces with respect to the weighting parameters are derived analytically based on the explicit expressions of dynamic responses of the controlled structure. Two numerical examples are investigated to demonstrate the feasibility of the EOC method and the optimization scheme for weighting parameters involved in the control law.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.62-65
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• 2004

An optimal robust controller design method for gun driving system is discussed in this paper. The parameters of the gun driving controller are tuned by using the LQR characteristics for the performance and robustness. Tuning method that optimize velocity error gives a significant improvement over the existing PID tuning methods. It is shown that the tuning result of real gun driving system which is regarded as rigidness model or stiffness model satisfy performance and robustness.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.3-6
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• 2001

This paper presents optimal robust PID controller design method for second order systems to satisfy the design specifications in time domain. The parameters of PID controller are determinated by the weighting factors Q and R of cost function. It is suggested that the selection of Q and R matrix can be determinated by its relationship with the natural frequency of ITAE criterion.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.551-556
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• 2014

This paper proposes a balancing control and driving control of a bicycle robot based on dynamic modeling of the bicycle robot, which has been derived using the Lagrange equations. For the balancing control of the bicycle robot, a reaction wheel pendulum method has been adopted in this research. By using the dynamics equations of the bicycle robot, an LQR controller has been designed for a balancing and driving control of a bicycle robot. The performance of the balance control is verified experimentally before the driving control, which shows a stable posture within one degree vibrations. To show the dynamic characteristics of the bicycle robot during driving, a trapezoidal velocity trajectory is selected as the references. Through simulations and real experiments, the effectiveness of the proposed algorithm has been demonstrated.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.721-730
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• 2018

This paper aims to inspect the effectiveness of the Simple Adaptive Control Method (SACM) to control the response of asymmetric buildings with rotationally non-linear behavior under seismic loads. SACM is a direct control method and was previously used to improve the performance of linear and non-linear structures. In most of these studies, the modeled structures were two-dimensional shear buildings. In reality, the building plans might be asymmetric, which cause the buildings to experience torsional motions under earthquake excitation. In this study, SACM is used to improve the performance of asymmetric buildings, and unlike conventional linear models, the non-linear inertial coupling terms are considered in the equations of motion. SACM performance is compared with the Linear Quadratic Regulator (LQR) algorithm. Moreover, the LQR algorithm is modified, so that it is appropriate for rotationally non-linear buildings. Active tuned mass dampers are used to improve the performance of the modeled buildings. The results show that SACM is successful in reducing the response of asymmetric buildings with rotationally non-linear behavior under earthquake excitation. Furthermore, the results of the SACM were very close to those of the LQR algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.45-52
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• 2006

In this study, a nonlinear control by feedback linearization method, one of nonlinear control schemes based on the nonlinear model, is proposed to suppress the flutter of a supersonic composite panel using piezoelectric materials. Most of the previous panel flutter controllers are the LQR(Linear Quadratic Regulator) which is based on the linear model. A nonlinear feedback linearizing controller proposed in this study considers the nonlinear characteristics of the system model. We use the actuator implemented by piezoceramic PZT. Using the principle of virtual displacements and a finite element discretization with the conforming four-node rectangular element, we first derive the discretized dynamic equations of motion, which are transformed into a nonlinear coupled-modal equations of motion of state space form. The effectiveness of the proposed method is also compared with the LQR based on the linear model through numerical simulations in the time domain using the Newmark method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.5
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• pp.37-42
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• 2004

This paper presents an optimal and robust tuning method of decentralized PI controller for the TITO second order systems to be formulated as LQR. The procedure is developed by establishing relationships between the closed-loop state equation including the decentralized PI tuning parameter and the . closed-loop state equation of LQR and by selecting the weighting factors Q and R of the cost function in order to satisfy the design specifications In frequency domain which the stability robustness and satisfied the performance guaranteed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.17-24
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• 2004

This paper proposes an optimal robust LQ-PID controller design method for the second order systems to satisfy the design specifications in time domain. The tuning parameters of LQ-PID controller are determinated by the relationships between the design parameters of the overshoot and the settling time which are design specifications in time domain, and the weighting factors Q and R in LQR. we can achieve the performance-robustness in time domain as well as the stability-robustness.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1014-1019
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• 2005

In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.