• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.518-526
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• 2008

The fuzzy self-tuning PID controller is a PID controller with a fuzzy logic mechanism for tuning its gains on-line. In this structure, the proportional, integral and derivative gains are tuned on-line with respect to the change of the output of system under control. This paper deals with two types of fuzzy self-tuning PID controllers, rule-based fuzzy PID controller and learning fuzzy PID controller. As a medical application of fuzzy PID controller, the proposed controllers were implemented and evaluated in a laparoscopic surgery robot system. The proposed fuzzy PID structures maintain similar performance as conventional PID controller, and enhance the position tracking performance over wide range of varying input. For precise approximation, the fuzzy PID controller was realized using the linear reasoning method, a type of product-sum-gravity method. The proposed controllers were compared with conventional PID controller without fuzzy gain tuning and was proved to have better performance in the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.349-356
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• 2006

A cartpole system is controlled by a control system consisting of two fuzzy controllers cooperating by a CMAC. Each controller uses 2 different input variables and yields the control force provided to the CMAC. The cooperation is due to training of the CMAC supervised by a judge which selects training information for the CMAC between two fuzzy controllers. The control scheme could be appreciated in terms of the tight structure of the controller, simple cooperating scheme due to the CMAC training, and accomplishing control goal that could not be attained by individual controllers.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.23-29
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• 2016

This paper presents balancing control of a two-wheel mobile robot (TWMR). TWMR is aimed to maintain balance while moving. Although TWMR can be controlled by linear controllers such as PD controller, time-delayed controller is employed for robustness. Performances of PD controllers and time-delayed controllers are compared. Especially, experimental studies on different acceleration estimation for the time-delayed controller are conducted. Performances by different acceleration estimations of the balancing angle, of the position, and of both angle and position are compared empirically.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.216-223
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• 2016

This paper proposes a new controller design method for a fork-type lifter (FTL) of a transportation mobile robot. The transportation robot needs to pick up a package from a stack on a storage shelf and move on by a planned path in a logistics center environment. The position of the storage shelf is recognized by reading a QR code on the floor, and using this position, the robot can move to reach the storage shelf and pick up the package. PID controllers and an adaptive controller are designed to control the velocity of two wheels and the position of the FTL. An adaptive controller for the lifter is designed to elevate up and down on a slideway to the correct height position of the package on the stack of the storage shelf. The simulation results show that the PID controllers can respond smoothly to the desired angular velocity and the adaptive controller can adapt quickly and correctly to the desired height.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.93-99
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• 2003

One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1） their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2） their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.847-852
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• 1993

In this paper, we present a recursive algorithm for the auto-tuning of PID controllers by optimizing a GPC criterion. Also, we develop an intelligent PID controller by combination of a recursive algorithm together with a supervisor, that allows to adjust the main controller parameters (prediction horizon, control weighting, sample time etc.) using some simple rules which is mainly built up through relay tuning experiments. The intelligent PID controller has been implemented successfully on an IBM PC/AT and some simulation results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.360-365
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• 2003

In this paper, we have developed a general purpose motion controller using an FPGA(Field Programmable Gate Array). The multi-PID controllers on a single chip are implemented as a system-on-chip for multi-axis motion control. We also develop a PC GUI for an efficient interface control. Comparing with the commercial motion controller LM 629 it has multi-independent PID controllers so that it has several advantages such as space effectiveness, low cost and lower power consumption. In order to test the performance of the proposed controller, robot finger is controlled. The robot finger has three fingers with 2 joints each. Finger movements show that position tracking was very effective. Another experiment of balancing an inverted pendulum on a cart has been conducted to show the generality of the proposed FPGA PID controller. The controller has well maintained the balance of the pendulum.

• Ocean Systems Engineering
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• v.4 no.3
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• pp.243-261
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• 2014

Aqua is an underwater biomimetic vehicle designed and built at McGill University that uses six paddles to produce control and propulsion forces. It has the particularity of having time-periodic thrust due to its oscillating paddles. Using an existing model of the vehicle, two types of controller were developed: a PD controller and a Floquet controller. The Floquet controller has the advantage of explicitly addressing the time-periodicity of the system. The performance of the controllers was assessed through simulation and experimentally in the Caribbean Sea. We find that the vehicle was able to follow the prescribed trajectories with relative accuracy using both controllers, though, the Floquet controller slightly outperforms the PD controller. Furthermore, a key advantage of the Floquet controller is that it requires no tuning while the PD controller had to be tuned by trial and error.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.710-717
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• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.302-307
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• 2005

A novel neuro controller based simple neuro-structure with modified error function is introduced in this paper. This controller consists of two independent controllers, known as the voltage regulator and the angular controller. The voltage regulator is used to modify terminal voltage for the purpose of tracking a reference voltage. The angular controller is utilized to guarantee the stability of the system. In this structure each neuron uses a linear hard limit activation function that depends on the controlled variable and its derivatives. There is no need for parameter identification or any off-line training data. Two proposed controllers are merged by a smooth switch to build a complete controller. The effectiveness of the proposed novel control action is demonstrated through some computer simulations on a Single-Machine Infinite-Bus (SMIB) power system.