• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.58-64
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• 1994

In this paper, a robust control scheme of a nonlinear system with norm bounded uncertainty is studied. The proposed algorithm is based on variable structure systems (VSS) theory. the sliding mode which is robust to plant uncertainty and disturbances is obtained by regulating a sliding surface equation. This VSS control law can improve the robustness of control systems by adjusting the minimum reaching velocity in a reaching phase. A numerical example is given to verify the effectiveness of the control law.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.605-613
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• 2001

In this paper, we design a robust adaptive controller for a nonlinear system with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered has unknown nonlinear functions being influenced by external disturbance. The upper bound of unknown nonlinear functions at each time is estimated by using a disturbance adaptation law. The estimated nonlinear functions are used to design a stabilizing function a control input. Tuning function is used to estimates unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically. The effectiveness of the proposed controller is investigated by computer simulation.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2187-2195
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• 2018

In order to realize the multi-objective control of single-input multi-output nonlinear differential algebraic system (NDAS) and to improve the dynamic characteristics and static accuracy, a design method of nonlinear control with multi-objective feedback (NCMOF) is proposed, the principium of this method to arrange system poles, as well as its nature to coordinate dynamic characteristics and static accuracy of the system are analyzed in detail. Through NCMOF design method, the multi-objective control of the system is transformed into linear space, and then it is effectively controlled under the nonlinear feedback control law, the problem to balance all control objectives caused by less input and more output of the system thus is solved. Applying NCMOF design method to generator excitation system, the nonlinear excitation control law with terminal voltage, active power and rotor speed as objective outputs is designed. Simulation results show that NCMOF can not only improve the dynamic characteristics of generator, but also damp the mechanical oscillation of a generator in transient process. Moreover, NCMOF can control the terminal voltage of the generator to the setting value with no static error under typical disturbances.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.1
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• pp.63-72
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• 1992

A tracking controller which can improve the performance of nonlinear electrohydraulic position control system is designed and implemented. The method is based on augmenting the system with integrators, obtaining the feedback control law which stabilizes the linear part of the original nonlinear system, and then reajusting the feedback gains using the deseribing funtion method to eliminate the limit cycle in the steady state. The proposed control law is implemented using OP amplifiers, and step and ramp response tests are carried out in the electrohydraulic servomechanism. The results show the improvement in both rransient and steady-state response.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.630-642
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• 2007

In this paper, we propose a direct model reference adaptive fuzzy control (MRAFC) for MIMO nonlinear systems whose structure is represented by the Takagi-Sugeno fuzzy model. The adaptive law of the MRAFC estimates the approximation error of the fuzzy logic system so that it provides asymptotic tracking of the reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. To verify the validity and effectiveness of the MRAFC scheme, the suggested analysis and design techniques are applied to the tracking control of robot manipulator and simulation studies are carried out. In the control design, the MRAFC is combined with feedforward PD control to make the actual joint trajectories of the robot manipulator with system uncertainties track the desired reference joint position trajectories asymptotically stably.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.131-136
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• 2012

If one can find a control Lyapunov function (CLF) for a given nonlinear system, the control input stabilizing the system can be easily obtained. To find a CLF, the time derivative of an energy function should be negative definite. This procedure frequently requires a control input which is a rational function or includes an inverse function. The control input is not defined on the specific state-space where the denominator of the rational function is equal to 0 or the inverse function does not exist. In this region with singularities, the trajectory of the control system cannot be generated, which is one of the most important reasons why it is hard to make the origin of a nonlinear system be globally asymptotically stable. In this paper, we propose a smooth control law ensuring the globally asymptotic stability by means of cancelling the singularity in the control input.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1005-1007
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• 2005

A Nonlinear approach to control of Small Scale Rotary Wing Unmanned Aerial Vehicle (R-UAV) is presented. Using Backstepping, a globally stabilizing control law is derived. We derive backstepping control law for angle of attack and sideslip control. The inherent nonlinear nature of the system are considered here which helps in naturally stabilizing without extensive external effort. Thus, the resulting control law is much simpler than if the feedback linearization had been used.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.379-387
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• 2007

In this paper, a nonlinear anti-sway controller for container cranes with load hoisting is investigated. The considered container crane involves a planar motion in conjunction with a hoisting motion. The control inputs are two (trolley and hoisting forces), whereas the variables to be controlled are three (trolley position, hoisting rope length, and sway angle). A novel feedback linearization control law provides a simultaneous trolley-position regulation, sway suppression, and load hoisting control. The performance of the closed loop system is shown to be satisfactory in the presence of disturbances at the payload and rope length variations. The advantage of the proposed control law lies in the full incorporation of the nonlinear dynamics by partial feedback linearization. The uniform asymptotic stability of the closed-loop system is assured irrespective of variations of the rope length. Simulation and experimental results are compared and discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2474-2479
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• 2005

The control objectives in this paper are to move the gantry of a container crane to its target position and to suppress the transverse vibration of the payload. The crane system is modeled as an axially moving nonlinear string equation, in which control inputs are applied at both ends, through the gantry and the payload. The dynamics of the moving string are derived using Hamilton's principle. The Lyapunov function method is used in deriving a boundary control law, in which the Lyapunov function candidate is introduced from the total mechanical energy of the system. The performance of the proposed control law is compared with other two control algorithms available in the literature. Experimental results are given.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.