• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.476-482
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• 2006

Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.154-160
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• 2004

In this paper, a combined field orientation and adaptive backstepping approach using a sliding mode adaptive flux observer, is proposed for the control of induction motor In order to achieve the speed regulation with the consideration of improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer based on a fixed stator frame model and mechanical lumped uncertainty such as inertia moment, load torque disturbance, friction compensated by the adaptive backstepping based on a field-oriented model. Simulation results are provided to verify the effectiveness of the proposed approach.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.1833-1844
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• 2004

A controller is proposed for the robust backstepping control of a class of nonlinear multiple-input multiple-output (MIMO) systems which can be converted to a strict feedback form. The proposed robust backstepping control scheme follows a systematic procedure for the design of control laws and uses time delay estimation (TDE) to estimate the uncertainties such as parameter variations, unknown disturbances, and unmodeled dynamics, etc. The proposed controller can be also applied to nonlinear MIMO systems with unmatched uncertainties. Stability analysis of the closed-loop system which contains the plant and the proposed controller is also studied and hereby a sufficient stability condition for the closed-loop system is proposed. The simulation results show that the control scheme works well with uncertainties and the proposed stability condition is valid. The controller is experimentally verified on a single-link flexible arm to show the effectiveness of the proposed scheme in the complicated systems with uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.329-339
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• 2003

A neural network based adaptive controller design method is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness decrease caused by control surface damage. The neural network based adaptive nonlinear controller is developed by making use of the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line teaming neural networks are implemented to guarantee reconfigurability and robustness to the uncertainties caused by aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed with assumption that not any of the nonlinear functions of the system is known accurately, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks loam through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated according to the Lyapunov theory. A nonlinear dynamic model of an F-16 aircraft is used to demonstrate the effectiveness of the proposed control law.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.902-911
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• 2012

This paper proposes a robust adaptive fuzzy backstepping control scheme for trajectory tracking of an electrically driven nonholonomic mobile robot with uncertainties and actuator dynamics. A complete model of an electrically driven nonholonomic mobile robot described in this work includes all models of the uncertain robot kinematics with a nonholonomic constraint, the uncertain robot body dynamics with uncertain frictions and unmodeled disturbances, and the uncertain actuator dynamics with disturbances. The proposed control scheme uses the backstepping control approach through a kinematic controller and a robust adaptive fuzzy velocity tracking controller. The presented control scheme has a voltage control input with an auxiliary current control input rather than a torque control input. It has two FBFNs(Fuzzy Basis Function Networks) to approximate two unknown nonlinear robot dynamic functions and a robust adaptive control input with the proposed adaptive laws to overcome the uncertainties such as parameter uncertainties and external disturbances. The proposed control scheme does not a priori require the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. It can also alleviate the chattering of the control input. Using the Lyapunov stability theory, the stability of the closed-loop robot control system is guaranteed. Simulation results show the validity and robustness of the proposed control scheme.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.711-723
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• 2017

This paper proposes a flight control system for an organic flight array(OFA) which has a new configuration to consist of multi modularized ducted-fan unmanned aerial vehicles (UAVs). The OFA is able to apply to various missions such as indoor reconnaissance, communication relay, and radar jamming by using capability of hover flight. The OFA has a distinguished advantage due to reconfigurable structure to assemble or separate with respect to its missions or operational conditions. A dynamic modelling of the OFA is derived based on equations of motion of the single ducted-fan modules. In order to apply nonlinear control method, an affine system of attitude dynamics is derived. Moreover, the control system is composed of a back-stepping controller for attitude control and a PID controller for position control. Then the performance of the proposed controller is verified via a numerical simulation under wind disturbance.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.494-499
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• 2013

Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.3
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• pp.119-128
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• 2005

In this paper, an robust adaptive backstepping controller is proposed for the chaos system with disturbances. This controller will be applicable to the chaos system of strict-feedback form and utilize the saturation function for decreasing the effect of disturbances derived from unmodelled dynamics and external noise. It shows that backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.77-78
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• 2008

This paper presents to control speed of induction motors with uncertainties. We use an adaptive backstepping controller with fuzzy neural networks(FNNs) and model reference adaptive system(MRAS) at Indirect vector control method. The adaptive backstepping controller using FNNs can control speed of induction motors even we have a minimum of information. And this controller can be used to approximate most of uncertainties which are derived from unknown motor parameters, load torque such as disturbances. MRAS estimates to rotor resistance and also can find optimal flux to minimize power losses of Induction motor. Indirect vector PI current controller is used to keep rotor flux constant without measuring or estimating the rotor flux. Simulation and experiment results are verified the effectiveness of this proposed approach.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.779-781
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• 1999

In this paper we consider the TORA system and use backstepping to design active controllers for tracking; this problem is much more challenging than stabilization. We show that the control effort of the closed-loop system can be significantly improved by exploiting the backstepping design.