• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.476-482
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• 2010

In this paper, we propose a robust path tracking control method for autonomous underwater vehicle with variable speed. The proposed path tracking controller consists of a kinematic controller and a dynamic controller. First, the kinematic controller computes the surge speed and yaw rate to follow the reference path with variable speed. Then the dynamic controller controls the thrust force and yaw torque to move the AUV actually. In the dynamic control, we assume that the sway speed is a disturbance. In addition the dynamic controller is designed based on sliding mode conrol. We also demonstrate the stability of the proposed control method by Lyapunov stability theory. Finally, simulation results illustrate the performance of the proposed control method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.7
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• pp.81-89
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• 2002

Guidance laws can be conceptually classified into three categories although their mathematical representations are various and different. In this paper, a generalized conceptual guidance law including the concepts of the above categories is proposed. The aim angle is introduced using the geometry of the collision triangle. The aim angle represents the arbitrary angle between the pursuit angle and the expected collision angle. The objective of the proposed guidance law is to make the aim angle zero asymptotically. It can be shown that the aim angle error response for the considered system is same as that of the first order system. When the autopilot of the missile system has slow dynamics, autopilot time lag may deteriorate the performance of the guidance law performance. In this case, another new guidance law compensating the autopilot time lag effect is proposed. To verify the proposed guidance laws, several numerical simulations are performed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.285-288
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• 2005

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose a neuro-adaptive controller for robot manipulators using the radial basis function network(RBFN) that is a kind of a neural network. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between the actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that the parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed neuro-adaptive controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1238-1245
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• 2014

This paper proposes an RBFN-based adaptive tracking controller for an electrically driven mobile robot with parametric uncertainties and external disturbances. A mobile robot model considered in this paper includes all models of the robot body and actuators with uncertain kinematic and dynamic parameters, and uncertain frictions and external disturbances. The proposed controller consists of an RBFN(Radial Basis Function Network) and a robust adaptive controller. The presented RBFN is used to approximate unknown nonlinear robot dynamic functions. The proposed controller is adjusted by the adaptation laws obtained through the Lyapunov stability analysis. The proposed control scheme does not a priori need the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. Also, nominal parameter values are not required in the controller. The global stability of the closed-loop robot control system is guaranteed using the Lyapunov stability theory. Simulation results show the validity and robustness of the proposed control scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.292-298
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• 2013

This paper considers synchronization problem of a complex dynamical network with nonidentical nodes. For the problem, the target node is chosen as one of nodes in the complex network instead of an isolate node. Moreover, our synchronization scheme does not need additional conditions and information of coupling matrix comparing with existing works. Based on Lyapunov stability theory, a design criterion for a novel adaptive feedback controller for the synchronization between the target node and another nodes of the complex network is proposed. Finally, the proposed method is applied to a numerical example in orther to show the effectiveness of our results.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.270-275
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• 2005

This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Doffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.744-747
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• 2010

This paper presents a novel adaptive control method for DC-DC converters applied in PV generator systems. We derive an state-space average model of the converter system and then propose a adaptive control methodology to enhance transient response performance for time-varying PV systems. A well-knwon Lyapunov theory is utilized for constructing our control algorithm. Numerical simulation demonstrates reliability of our control methodology and its superiority by comparison to a traditional control strategy.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.12
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• pp.691-699
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• 2004

SRM(Switched Reluctance Motor) drives require the accurate position information of the rotor. These informations are generally provided by a tacho generator or digital shaft-position encoder These speed sensors lower the system reliability and require special attention to noise. This paper describes a new approach to estimating SRM speed from measured terminal voltages and currents for speed sensorless control. The described method is based on the sliding mode observer. The rotor speed and position observers are estimated by the adaptation law using the real and estimated currents. However, the conventional adaptive sliding mode observer based on the variable structure control theory has some disadvantages that the estimated values including the high-frequency chattering and the steady state error generated due to the infinite feedback gain chosen and the discontinuous control input. To reduce the chattering and steady state error, an integrator is also inserted in the sliding mode observer strategy. The described adaptive sliding mode observer decreases the vibration to the switching hyper-plane of the sliding mode by adding integrator. The described methodology incorporates the Lyapunov algorithm to drive the rotor speed and the stator resistance such that it can overcome the problem of sensitivity in the face of SRM parameter variation. Also, without any mechanical information. The rotor speed of SRM is obtained form adaptive scheme. The described method is verified through the simulation and experiment.