• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.5
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• pp.817-832
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• 2015

This paper presents a state feedback based backstepping control algorithm to address the trajectory tracking problem of an underactuated Unmanned Surface Vessel (USV) in the horizontal plane. A nonlinear three Degree of Freedom (DOF) underactuated dynamic model for USV is considered, and trajectory tracking controller that can track both curve trajectory and straight line trajectory with high accuracy is designed as the well known Persistent Exciting (PE) conditions of yaw velocity is completely relaxed in our study. The proposed controller has further been enriched by incorporating an integral action additionally for enhancing the steady state performance and control precision of the USV trajectory tracking control system. Global stability of the overall system is proved by Lyapunov theory and Barbalat's Lemma, and then simulation experiments are carried out to demonstrate the effectiveness of the controller designed.

• Journal of IKEEE
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• v.23 no.2
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• pp.716-721
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• 2019

A novel switching differentiator-based backstepping controller for uncertain pure-feedback nonlinear systems is proposed. Using asymptotically convergent switching differentiator, time-derivatives of the virtual controls are directly estimated in every backstepping design steps. As a result, the control law has an extremely simple form and asymptotical stability of the tracking error is guaranteed regardless of parametric or unstructured uncertainties and unmatched disturbances in the considered system. It is required no universal approximators such as neural networks or fuzzy logic systems that are adaptively tuned online to cope with system uncertainties. Simulation results show the simplicity and performance of 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.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.859-872
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• 2008

In this paper, the problem of partial asymptotic stabilization of nonlinear control cascaded systems with integrators is considered. Unfortunately, many controllable control systems present an anomaly, which is the non complete stabilization via continuous pure-state feedback. This is due to Brockett necessary condition. In order to cope with this difficulty we propose in this work the partial asymptotic stabilization. For a given motion of a dynamical system, say x(t,$x_0,t_0$)=(y(t,$y_0,t_0$),z(t,$z_0,t_0$)), the partial stabilization is the qualitative behavior of the y-component of the motion(i.e., the asymptotic stabilization of the motion with respect to y) and the z-component converges, relative to the initial vector x($t_0$)=$x_0$=($y_0,z_0$). In this work we present new results for the adding integrators for partial asymptotic stabilization. Two applications are given to illustrate our theoretical result. The first problem treated is the partial attitude control of the rigid spacecraft with two controls. The second problem treated is the partial orientation of the underactuated ship.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.181-184
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• 1996

This paper deals with the design problem of model reference adaptive controllers for MIMO plants with unknown orders. A design scheme for an adaptive control system based on CGT theorem, which has hierarchical structures derived from backstepping strategies, is proposed for MIMO plants with unknown orders but with known relative MacMillan degrees(relative degrees for SISO plants). It is also shown that all the signals in the resulting control system are bounded, and that the asymptotic tracking is achieved in the case where reference inputs are step.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.54-67
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• 2003

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.78-83
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• 2003

In this paper, it is dealt with a controller design problem for nonlinear systems with higher order relative degree. A robust adaptive control for uncertain nonlinear systems with stable zero dynamics will be proposed based on the high-gain adaptive output feedback and backstepping strategies. The proposed method is useful in the case where only the output signal is available.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.3
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• pp.238-245
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• 2020

In this study, a motion control problem for the vessel towed by towing ship on the sea is considered. The towed vessel does not have self-control capabilities such that its course stability totally depends on the towing ship. Especially, in the narrow canal, river and congested harbor area, extreme tension is required during the towing operation. The authors, therefore, propose a new control system design method in which the rudder is activated to provide its maneuverability. Based on the leader following system configuration, a nonlinear mathematical model is derived and a backstepping control is designed. By experiment results with nonlinear control framework, the usefulness and effectiveness of the proposed strategy are presented.

• 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.

• Proceedings of the KIEE Conference
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• 2006.10b
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• pp.181-184
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• 2006

In this study, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor with uncertainties and disturbances. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the 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 and experiment results are provided to demonstrate the effectiveness of the proposed controller in the future.