• International Journal of Aeronautical and Space Sciences
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• v.12 no.2
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• pp.179-189
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• 2011

In this paper, robust adaptive control design problem is addressed for a class of parametrically uncertain aeroelastic systems. A full-state robust adaptive controller was designed to suppress aeroelastic vibrations of a nonlinear wing section. The design used leading and trailing edge control actuations. The full state feedback (FSFB) control yielded a global uniformly ultimately bounded result for two-axis vibration suppression. The pitching and plunging displacements were measurable; however, the pitching and plunging rates were not measurable. Thus, a high gain observer was used to modify the FSFB control design to become an output feedback (OFB) design while the stability analysis for the OFB control law was presented. Simulation results demonstrate the efficacy of the multi-input multi-output control toward suppressing aeroelastic vibrations and limit cycle oscillations occurring in pre- and post-flutter velocity regimes.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1077-1082
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• 2005

In this paper, it is proposed that the method for constituting pressure control system controlled by Direct Drive Valve (DDV). The DDV has a pressure-feedback-loop itself. It can eliminate non-linearity and uncertainty oi hydraulic system such as uncertain discharge coefficient and change of bulk-modulus. However, the internal feedback-loop can not compensate them perfectly. And fixed gain of the DDV's internal feedback-loop is not proper to apply it through wide pressure range. The steady state error and nonlinear characteristic of transient behaviour is observed in the experiment. So another controller is needed for the desirable performance of the system. To compose the controller, the pressure control system controlled by DDV is modeled mathematically and the parameters of the model are identified using signal-compression method. Then sliding mode controller is designed based on mathematical model. Desirable performance of the pressure control system controlled by DDV is obtained.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.11
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• pp.895-900
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• 2005

We derive a linear matrix inequality(LMI) condition guaranteeing that any invariant zeros of a triple (A, B, C) lie in the open left half plane of the complex plane, i.e. $C(sI-A)^{-1}B$ is minimum phase. The LMI condition is equivalent to a certain constrained Lyapunov matrix equation which can be found in many results relating to stability analysis or control design. We show that the LMI condition can be used to simplify various control engineering problems such as a dynamic output feedback control problem, a variable structure static output feedback control problem, and a nonlinear system observer design problem. Finally, we give some numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.48.1-48
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• 2002

The method of endowing the controller with the strengthened robustness within the boundary layer is presented for controlling the uncertain nonlinear systems in which the variations of the uncertainties are slow. From this controller, the width of the boundary layer where the robust control input is smoothened out can be given by an appropriate value but a better control performance within the boundary layer can be achieved without the control chattering because the role of adaptive control is to compensate for the uncovered portions of the robust control occurred from the continuous approximation within the boundary layer.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2283-2286
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• 2000

Sliding control is a powerful approach to controlling nonlinear and uncertain systems. Conventional sliding mode control suffer' from high control gain and chattering problem. also it needs mathematic! modeling equations for control systems. A Fuzzy controller is endowed with control rules and membership function that are constructed on the knowledge of expert, as like intuition and experience. but It is very difficult to obtain the exact values which are the membership function and consequent parameters. In this paper, without mathematical modeling equations, the plant parameters in sliding mode are estimated by the indirect adaptive fuzzy method. the proposed algorithm could analyze the system's stability and convergence behavior using Lyapunov theory. so sliding modes are reconstructed and decreased tracking error. moreover convergence time took a short. An example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.191-197
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• 2006

This paper deals with a fault detection system design for nuclear steam generator water level control system. We expressed the nonlinear properties of the steam generator level system as a T-S fuzzy system with time varying uncertain parameters. We design a residual generator using a left coprime factorization of the T-S fuzzy model and a fault detection filter in order to improve the fault detection performance. We demonstrate the efficiency of the suggested design method via many computer simulations.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1193-1202
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• 2019

A SMPMSM drive system is a typical nonlinear system with time-varying parameters and unmodeled dynamics. The speed outer loop and current inner loop control structures are coupled and coexist with various disturbances, which makes the speed control of SMPMSM drive systems challenging. First, an ultra-local model of a PMSM driving system is established online based on the algebraic estimation method of model-free control. Second, based on the backstepping control framework, model-free adaptive integral backstepping (MF-AIB) control is proposed. This scheme is applied to the permanent magnet synchronous motor (PMSM) drive system of an electric vehicle for the first time. The validity of the proposed control scheme is verified by system simulations and experimental results obtained from a SMPMSM drive system bench test.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.990-998
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• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Journal of Computing Science and Engineering
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• v.10 no.3
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• pp.75-84
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• 2016

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.487-492
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• 2007

This paper presents the development of digital robust fuzzy controller for uncertain nonlinear systems. The proposed approach is based on the intelligent digital redesign(IDR) method with considering the relaxed stability condition of fuzzy control system. The term IDR in the concerned system is to convert an existing analog robust control into an equivalent digital counterpart in the sense of the state-matching. We shows that the IDR problem can be reduced to find the digital fuzzy gains minimizing the norm distance between the closed-loop states of the analog and digital robust control systems. Its constructive conditions are expressed as the linear matrix inequalities(LMIs) and thereby easily tractable by the convex optimization techniques. Based on the nonquadratic Lyapunov function, the robust stabilization conditions are given for the sampled-data fuzzy system, and hence less conservative. A numerical example, chaotic Lorentz system, is demonstrated to visualize the feasibility of the proposed methodology.