• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.588-591
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• 1998

In this paper, we present a robust adaptive backstepping output feedback controller for nonlinear systems perturbed by unmodelled dynamics and disturbances. Especially, backstepping technique with modular approach is used to separately design controller and identifier. The design of identifier is based on the observer-based scheme which possesses a strict passivity property of observer error system. We will use Switching-${\sigma}$ modification at the update law and the modified control law to attenuate the effects of undodelled dynamics and disturbances for nonlinear systems.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.1-12
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• 2022

In order to overcome the power fluctuation issues in photovoltaic (PV) smart grid-connected systems and the inverter nonlinearity model problem, an adaptive backstepping command-filter and a double second order generalized Integrators (DSOGI) controller are designed in order to tune the AC current and the DC-link voltage from the DC side. Firstly, we propose to present the filter mathematical model throughout the PV system, at that juncture the backstepping control law is applied in order to control it, Moreover the command filter is bounded to the controller aiming to exclude the backstepping controller differential increase. Additionally, The adaptive law uses Lyapunov stability criterion. Its task is to estimate the uncertain parameters in the smart grid-connected inverter. A DSOGI is added to stabilize the grid currents and eliminate undesirable harmonics meanwhile feeding maximum power generated from PV to the point of common coupling (PCC). Then, guaranteeing a dynamic effective response even under very unbalanced loads and/or intermittent climate changes. Finally, the simulation results will be established using MATLAB/SIMULINK proving that the presented approach can control surely the smart grid-connected system.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1646_1647
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• 2009

In this paper, Takagi-Sugeno(T-S) fuzzy controller and search method are developed for efficiency optimization of induction motors(IMs). The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of T-S fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is used to control of speed of IMs. Simulation results are presented to validate the proposed controller.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.74-76
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• 2009

In this paper, search method and sliding mode observer are developed for efficiency optimization of induction motor. The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. The search controller is based on the "Rosenbrock" method and finds the flux level at the minimum input power of induction motor. Once this optimal flux level has been determined, this information is utilized to update the rule base of a fuzzy controller A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is also used to compensate for mechanical uncertainties in the speed control of induction motor. Simulation results are presented to validate the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Journal of IKEEE
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• v.14 no.3
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• pp.182-189
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• 2010

A new Adaptive neural state-feedback controller for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controller requires no backstepping design procedure. Avoiding backstepping makes the controller structure and stability analysis considerably simple. The proposed controller employs only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller. Simulation examples demonstrate the efficiency and performance of the proposed approach.

• Kyungpook Mathematical Journal
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• v.54 no.2
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• pp.293-320
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• 2014

In this paper, global chaos synchronization is investigated for WINDMI (J. C. Sprott, 2003) and Coullet (P. Coullet et al, 1979) chaotic systems using adaptive backstepping control design based on recursive feedback control. Our theorems on synchronization for WINDMI and Coullet chaotic systems are established using Lyapunov stability theory. The adaptive backstepping control links the choice of Lyapunov function with the design of a controller and guarantees global stability performance of strict-feedback chaotic systems. The adaptive backstepping control maintains the parameter vector at a predetermined desired value. The adaptive backstepping control method is effective and convenient to synchronize and estimate the parameters of the chaotic systems. Mainly, this technique gives the flexibility to construct a control law and estimate the parameter values. Numerical simulations are also given to illustrate and validate the synchronization results derived in this paper.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.128-131
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• 2005

In this paper a robust controller using adaptive backstepping technique is proposed to control the speed of a wind turbine system. To make wind power generation truly cost effective and reliable, advanced and robust control algorithms are derived to on-line adjust the excitation winding voltage of the generator based on both mechanical and electrical dynamics. This method is shown to be able to achieve smooth and asymptotic rotor position tracking, as justified by analysis.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1227-1229
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• 2007

In this paper a robust controller using adaptive backstepping technique is proposed to control the position of wind power generation system. To make wind power generation truly cost effective and reliable, advanced and robust control algorithms are derived to on-line adjust the excitation winding voltage of the generator based on both mechanical and electrical dynamics. This method is shown to be able to achieve smooth and asymptotic rotor speed tracking, as justified by analysis and computer simulation.