• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.4
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• pp.381-384
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• 2008

In this paper, we consider the problem of designing decentralized sliding mode static output feedback control laws for a class of large scale systems with mismatched uncertainties. We derive a sufficient condition for the existence of a linear switching surface in terms of constrained linear matrix inequalities(LMIs), and we parameterize the linear switching surfaces in terms of the solution matrices to the given constrained LMI existence conditions. We also give an LMI-based algorithm for designing decentralized switching feedback control laws. Finally, we give a design example in order to show the effectiveness of our method.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.31-39
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• 2000

We present a robust and reliable H$\infty$ state-feedback controller design for linear uncertain systems, which have norm-bounded time-varying uncertainty in the state matrix, and their prespecified sets of actuators are susceptible to failure. These controllers should guarantee robust stability of the systems and H$\infty$ norm bound against parameter uncertainty and/or actuator failures. Based on the linear matrix inequality (LMI) approach, two state-feedback controller design methods are constructed by formulating to a set of LMIs corresponding to all failure cases or a single LMI that covers all failure cases, with an additional costraint. Effectiveness and geometrical property of these controllers are validated via several numerical examples. Furthermore, the proposed LMI frameworks can be applied to multiobjective problems with additional constraints.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.509-512
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• 2012

This paper presents the state feedback control design for discrete time nonlinear systems where there exists a time delay in input. It is shown that under some boundedness condition, the time delay nonlinear systems can be transformed into the time delay linear systems with time varying parameters. Sufficient conditions for existence of stabilizing state feedback controller are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.96-100
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• 2003

In this paper, a DLC method is proposed for linear feedback systems to improve the tracking performance when the task of the system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions considered in this paper have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to generate additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.503-510
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• 2003

This paper describes the synthesis of robust and non-fragile $H^{i~}$state feedback controllers for linear varying systems with time delay and affine parameter uncertainties, as well as static state feedback controller with structural uncertainty. The sufficient condition of controller existence, the design method of robust and non-fragile $H^{i~}$static state feedback controller, and the region of controllers satisfying non-fragility are presented. Also, using some change of variables and Schur complements, the obtained conditions can be rewritten as parameterized Linear Matrix Inequalities (PLMIs), that is, LMIs whose coefficients are functions of a parameter confined to a compact set. We show that the resulting controller guarantees the asymptotic stability and disturbance attenuation of the closed loop system in spite of time delay and controller gain variations within a resulted polytopic region.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.233-236
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• 2015

In this paper, a nonlinear optimization problem is proposed to obtain a structured static output feedback controller for discrete time linear systems. The proposed optimization problem has LMI (Linear Matrix Inequality) constraints and a non-convex objective function. Using the conditional gradient method, we can obtain suboptimal solutions of the proposed optimization problem. Numerical examples show the effectives of the proposed approach.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.489-494
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• 2013

This paper tackles the problem of designing a dynamic output-feedback control for linear discrete-time norm-bounded uncertain systems with input saturation. By employing a LPV (Linear Parameter Varying) instead of LTI (Linear Time-Invariant) control, the useful information on interpolation parameters appearing in the procedure of representing saturation nonlinearity as a convex polytope is additionally applied in the control design procedure. By solving the addressed problem that can be recast into a convex optimization problem characterized by LMIs (Linear Matrix Inequalities) with one prescribed scalar, the vertices of convex set containing an LPV output-feedback control gain and the associated maximal invariant set of initial states are simultaneously obtained.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1955-1959
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• 2005

This paper presents a systematic analysis and a simple design of a robust adaptive control law for a class of non linear systems with modeling errors and a time-delay input. The theory for designing a robust adaptive control law based on input- output feedback linearization of non linear systems with uncertainties and a time-delay in the manipulated input by the approach of parameterized state feedback control is presented. The main advantage of this method is that the parameterized state feedback control law can effectively suppress the effect of the most parts of nonlinearities, including system uncertainties and time-delay input in the pp-coupling perturbation form and the relative order of non linear systems is not limited.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.260-269
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• 2000

The fuzzy and state-feedback control systems have been applied in various areas from non-linear to linear 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. though apply back-propagation algorithm to the system, the convergence time a much. Besides, the state-feedback system is most widely used in industry due to its simple control structure and easily able to design the controller. but it is weak in complex system of higher degree and non-linear. In this paper presents the design of a fuzzy switching mode, it these two controllers work at different operation conditions, the advantages of both controller can be retained and the disadvantages can be removed. Between the Fuzzy and the State-feedback controlles, the good outputs are selected by the switching mode. Moreover it is powerful in complex system of higher degree and non-linear. In these sense compared with the state-feedback controller, the performance of the proposed controller was improvedin the section of linearization.