• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.199-204
• /
• 2006

In this paper, we propose an adaptive control algorithm to improve the position accuracy and reduce the nonlinear friction effects for linear motion servo system. Especially, the considered system includes not only the variation of the mass of the mover but also the friction change by the normal force. To adapt to these problems, we designed the controller with the mass estimator and the compensator by observing the variation of normal force. Finally, the numerical simulation results are presented in order to show the effectiveness of the proposed method to improve the position accuracy compared to other control methods.

• Journal of Drive and Control
• /
• v.16 no.2
• /
• pp.59-65
• /
• 2019

Hydraulic systems have severe nonlinearity inherently compared to other systems like electric control systems. Hence, precise modeling and analysis of the hydraulic control systems are not easy. In this study, the control performance of a hydraulic control system with a feedback linearization compensator and a disturbance observer was analyzed through experiments and numerical simulations. This study mainly focuses on the quantitative investigation of sensitivity on system uncertainties in the hydraulic control system. First, the sensitivity on the system uncertainty of the hydraulic control system with a Feedback Linearization - State Feedback Controller (FL-SFC) was quantitatively analyzed. In addition, the efficacy of a disturbance observer coupled with the FL-SFC for the hydraulic control system was verified in terms of overcoming the control performances deterioration owing to system uncertainty.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.405-413
• /
• 2006

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it an other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstepping control method.

• Journal of Navigation and Port Research
• /
• v.31 no.1 s.117
• /
• pp.55-64
• /
• 2007

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it and other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstepping control method.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.2
• /
• pp.58-69
• /
• 1998

A contrp; suste, fpr SCARA robot is designed for implememting a robust dynamic control algorithm. this study forcuses on the use of DSPs in the design of joint controllers and interfaces in between the host cotroller and four joint controllers and in between the joint controllers and four servo drives. The mechanical body of SCARA robot and the servo drives are selected from the commercially available ones. The four joint controllers, assigned to each joint one by one, are combined into a common system through a mother board hardwarewise and through the global memeory softwarewise. The mother board is designed to connect joint controllers onto the board through the slots adopting PC/104 bus structures. And, the global memory stores the common data which can be shared by joint controllers and the host computer directly, which virtually combines the whole system into one. To demonstrate the performance and efficienty of the sytem, a robust inverse dynamic algorithm is proposed and implemented for a faster and more precise control. The robust inverse dynamic algorithm is basically derived from an inverse dynamci algorithm and a PID compensator. Based upon the derived dynamic equitions of SCARA robot, the inverse dynamic algorithm is intitially implemented within 0.3 msec of the control cycle in this system. The algoithm is found to be not accurate enough for the high speed and precision tasks due to inherent modelling errors and time-varying factors. Therefore, a variable PID algorithm is combined with the inverse dynamic algorithm to support robustness of control performance. Experimental datfor the proposed algorithm are presented and compared with the result obtained from PID and inverse dynamic algorithm.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.7
• /
• pp.1234-1243
• /
• 1992

A systematic procedure to design fuzzy PID controllers is developed in this paper. The concept of local fuzzy control cell is proposed by introducing both an adequate global control rule and membership functions to simplify a fuzzy logic controller. Fuzzy decision is made by using algebraic product and parallel firing arithematic mean, and a defuzzification strategy is adopted for improving the computational efficiency based on nonfuzzy micro-processor. A direct method, transforming the typical output of quasi-linear fuzzy operator to the digital compensator of PID form, is also proposed. Finally, the proposed algorithm is applied to an DC-servo motor. It is found that this algorithm is systematic and robust through computer simulations and implementation of controller using Intel 8097 micro-processor.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.3
• /
• pp.49-54
• /
• 2003

In this paper, we propose a controller with the self-organizing neural network compensator for compensating PID controller's response. PID controller has simple design method but needs a lot of trials and errors to determine coefficients. A neural network control method does not have optimal structure as the parameters are pre-specified by designers. In this paper, to solve this problem, we use a self-organizing neural network which has Back Propagation Network algorithm using a Gaussian Potential Function as an activation function of hidden layer nodes for compensating PID controller's output. Self-Organizing Neural Network's learning is proceeded by Gaussian Function's Mean, Variance and number which are automatically adjusted. As the results of simulation through the second order plant, we confirmed that the proposed controller get a good response compare with a PID controller. And we implemented the of controller performance hydraulic servo motor system using the DSP processor. Then we observed an experimental results.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.5
• /
• pp.598-603
• /
• 2004

An optical head actuator of an optical disk drive consists of two servo mechanisms for the focusing and the tracking to acquire data from disk. As the rotational speed of the disk grows, the utilized lag-lead-lead compensator has known to be above its ability for precisely controlling the optical head actuator. To overcome the difficulty, this paper propose a new controller design method for optical head actuator based fuzzy proportional-integral-derivative (PID) control and the genetic algorithm(GA). It employs a two-stage control structure with a fuzzy PI and a fuzzy PD control and is optimized by the GA to yield the suboptimal fuzzy PID control performance. It is shown the feasibility of the proposed method through a numerical tracking actuator simulation.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.49 no.8
• /
• pp.557-564
• /
• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system can be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. In addition, the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Signal Processor DS1102 Board (TMS320C31).

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.2
• /
• pp.138-142
• /
• 2004

This paper proposes the design of the model following control system using the PID control structure. PID control system became model following control by inserting new pre-compensator in order to improve control performance in discrete-time region. Gain of the PID controller needs to be readjusted when response of system changes due to disturbance or load fluctuation. Performance of control system improves by joining neural network to PID control system because performance of control system depends largely on each PID gain in PID control system. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Angular position of DC servo motor is selected as a plant in order to verify control performance in model following control. After it is applied to the position control system, it's performance is verified through computer experiment.