• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.888-894
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• 2005

In this paper, experimental studies of balancing a pendulum mounted on a wheeled drive mobile robot and its position control are presented. Main PID controllers are compensated by a neural network. Neural network learning algorithm is embedded on a DSP board and neural network controls the angle of the pendulum and the position of the mobile robot along with PID controllers. Uncertainties in system dynamics are compensated by a neural network in on-line fashion. Experimental results show that the performance of balancing of the pendulum and position tracking of the mobile robot is good.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.213-218
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• 1991

A new model which contains the dynamics of the motor system and the kinematics of the timing belt system is derived for an inverted pendulum system in FAPA Lab. Generalized standard compensator configuration(SCC) which contains the variable design parameters Kl, K2, .., K5 is proposed so that any desired design specification can be achieved. The robust controller which has robust property against the influence of sensor noise, system parameter variation and model uncertainty is designed minimizing the H$_{\infty}$-norm of transfer function from exogenous input to controlled output. The method of solving the two Riccati equations in state space and determining the controller uses on iteration method where the unique stabilizing solution to two algebraic Riccati equation must be positive definite and the spectral radius of their product less than .gamma.$^{2}$. Some cases are derived by varying the design parameter for simulation on a digital computer and experimenting the H$_{\infty}$- controller on an analog computer. The design parameters of controller which satisfies the desired control specification is selected on the basis of the simulation result and experimenting. The reasonableness and validity of the simulation and the robustness of the controller is established.d.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.11
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• pp.9-21
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• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.107.4-107
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• 2001

In this paper, the guaranteed stability margins of LQ optimal regulators for a performance index with cross-product terms are suggested in the discrete-time case. An example for the LQ optimal regulator of an inverted pendulum system is given to demonstrate the validity of the suggested stability margins.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.499-502
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• 1996

In this paper, we propose a fuzzy iterative learning controller(FILC). It can control fully unknown dynamic plants through iterative learning. To design learning controllers based on the steepest descent method, it is one of the difficult problems to identify the change of plant output with respect to the change of control input(.part.e/.part.u). To solve this problem, we propose a method as follows: first, calculate .part.e/.part.u using a similarity measure and information in consecutive time steps, then adjust the fuzzy logic controller(FLC) using the sign of .part.e/.part..u. As learning process is iterated, the value of .part.e/.part.u is reinforced. Proposed FILC has the simple architecture compared with previous other controllers. Computer simulations for an inverted pendulum system were conducted to verify the performance of the proposed FILC.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.834-838
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• 2013

This paper proposes a new ball robot which has a four axis structure and four motors that directly actuate the ball to move or to maintain the balance of the robot. For the Balancing control, it is possible to use non-model-based controller to control simply without complex formula. All the gains of the controller are heuristically adjusted during the experiments. The tilt angle is measured by IMU sensors, which is used to generate the control input of the roll and pitch controller to make the tilt angle zero. The performance of the designed control system has been verified through the real experiments with the developed ball robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.271-276
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• 2006

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2074-2079
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• 2018

Global state space's optimal policy is used for offline controller in the form of table by using Dynamic Programming. If an optimal policy table has a large amount of control data, it is difficult to use the system in a low capacity system. To resolve these problem, controller using the compressed optimal policy table is proposed in this paper. A DCT is used for compression method and the cosine function is used as a basis. The size of cosine function decreased as the frequency increased. In other words, an essential information which is used for restoration is concentrated in the low frequency band and a value of small size that belong to a high frequency band could be discarded by quantization because high frequency's information doesn't have a big effect on restoration. Therefore, memory could be largely reduced by removing the information. The compressed output is stored in memory of embedded system in offline and optimal control input which correspond to state of plant is computed by interpolation with Inverse DCT in online. To verify the performance of the proposed controller, computer simulation was accomplished with a one link inverted pendulum.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.232-239
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• 2015

Studies on the control of the inverted pendulum type system have been widely reported. This is because it is a typical complex nonlinear system and may be a good model for verifying the performance of a proposed control system. In this paper, we propose the design of some fuzzy logic control (FLC) systems for controlling a Segway-type mobile robot, which is an inverted pendulum type system. We first derive a dynamic model of the Segway-type mobile robot and then analyze it in detail. Next, we propose the design of some FLC systems that have good performance for the control of any nonlinear system. Then, we design two conventional FLC systems for the position and balance control of the Segway-type mobile robot, and we demonstrate their usefulness through simulations. Next, we point out the possibility of simplifying the design process and reducing the computational complexity,, which results from the skew symmetric property of the fuzzy control rule tables. Finally, we design two other FLC systems for position and balance control of the Segway-type mobile robot. These systems have only one input variable in the FLC systems. Furthermore, we observe that they offer similar control performance to that of the conventional two-input FLC systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.752-758
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• 2004

In this paper, we proposed an adaptivefuzzy sliding control algorithm using gradient descent method to reduce chattering phenomenon which is viewed in variable control system. In design of FLC, fuzzy control rules are obtained from expert's experience and intuition and it is very difficult to obtain them. We proposed an adaptive algorithm which is updated by consequence part parameter of control rules in order to reduce chattering phenomenon and simultaneously to satistfy the sliding mode condition. The proposed algorithm has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.