• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.24 no.4
• /
• pp.33-42
• /
• 2010

The induction motor drive applied to field oriented control is widely used in industry applications. However, it is deceased performance and authenticity by saturation, temperature changing, disturbance and parameters changing because modeling of induction motor is nonlinear and complex. In order to control variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper proposes self tuning PI controller based on fuzzy-neural network(FNN)-PI controller that is implemented using fuzzy control, neural network, and adaptive fuzzy controller(AFC). Also, this paper proposes estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FNN-PI, AFC and ANN controller. Also, this paper proposes the anlysis results to verify the effectiveness of controller.

• Journal of Power System Engineering
• /
• v.20 no.2
• /
• pp.58-65
• /
• 2016

Lately, tugboats are widely used to maneuver vessels by pushing or towing them where tugboats use rope. In order to correctly control the motion of tugboat and towed vessel, the dynamics of the towline would be well identified. In real application environment, the towing rope length changes and the towing load is not constant due to the various sizes of towed vessel. And there are many ropes made by many types of materials. It means that it is not easy to obtain rope dynamics, such that it is too difficult to satisfy the given control purpose by designing control system. Thus real time identification or adaptive control system design method may be a solution. However it is necessary to secure sufficient information about rope dynamics to obtain desirable control performance. In this paper, the authors try to have several rope dynamic models by changing the rope length to consider real application conditions. Among them, a representative model is selected and the others are considered as uncertain models which are considered in control system design. The authors design a robust control to cope with strong uncertain and nonlinear property included in the real plant. The designed control system based on robust control framework is evaluated by simulation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.5
• /
• pp.320-325
• /
• 2016

This paper presents a robust finite-time sliding mode control (SMC) scheme for unknown disturbance and unmodeled nonlinear friction and dynamics in the robotic manipulator. A finite-time SMC (FSMC) surface and finite-time sliding mode controller are constructed to obtain faster error convergence than the conventional infinite-time based SMC. By adding prescribed constraint control term to a finite-time SMC to compensate for unknown disturbance and uncertainties, a robust control scheme can be designed as well as faster convergence control. In addition, simpler controller structure is built by using feed-forwarding upper bound coefficients of each manipulator dynamic parameters instead of model-based control or adaptive observer to estimate unknown manipulator parameters. Simulation and experimental evaluations highlight the efficacy of the proposed control scheme for an articulated robotic manipulator.

• Journal of Advanced Navigation Technology
• /
• v.13 no.6
• /
• pp.813-819
• /
• 2009

Designing actuator controllers of aircraft, which control aileron, flap, elevator and so on, is quiet difficult, because they have time variant nonlinear mechanical structures and also have many kinds of disturbances which are not been able to model easily. This paper reports about the performance of Fuzzy Auto gain tuning Control algorithm applied unmaned aerial vehicle. Fuzzy Auto gain tuning PID control uses PID control and Fuzzy control, therefore It can be applied very easily and it also has advances of PID control. It can control a unmaned aerial vehicle actuators adaptively even though the designer does not have enough information of plant.

• Structural Engineering and Mechanics
• /
• v.89 no.2
• /
• pp.213-223
• /
• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.7
• /
• pp.75-84
• /
• 2003

To reduce the effects of the uncertainties due to the modeling error and aerodynamic coefficients, a nonlinear adaptive control system based on neural networks is proposed . Neural networks parameters are adjusted by using an adaptive law. The sliding mode control scheme is used to compensate for the effect of the approximation error of neural networks. Control parameters and neural networks structures are optimized to obtain better performance by using the genetic algorithm. By introducing the concept of multi-groups of populations, the genetic algorithm is modified so that individuals and groups can be simultaneously evolved . To verify the performance of the pro posed algorithm, the optimized neural networks control system is applied to an aircraft longitudinal dynamics.

• Journal of Power Electronics
• /
• v.11 no.4
• /
• pp.393-400
• /
• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.1
• /
• pp.1211-1217
• /
• 2001

This paper presnts a controller for the heat capacity of thermal storage systems using off-peak electricity which is composed of an identifier using neural networks and a storage time adjuster in order to store exactly the required thermal energy without loss. Since thermal storage systems have nonlinear characteristics and large time constant, even if we predict the heating load accurately, it is very difficult to store exactly the required thermal energy. Thus, in the neural network for the identifier, the adaptive learning rate for high learning speed and bit inputs based on state changes of thermal storage power source are used. Also a hardware for the controller using a microprocessor is developed. The performance of the proposed controller is shown by experiment.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.4
• /
• pp.191-202
• /
• 2013

A control structure that makes possible the integration of a kinematic controller and a fuzzy logic (FL) deadzone compensator for mobile robots is presented. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a mobile robot to show its efficacy.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2513-2515
• /
• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for leaming in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of Butter-Worth analog filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate Butter-Worth analog filter parameter using the genetic algorithm.