• Earthquakes and Structures
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• v.21 no.6
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• pp.577-585
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• 2021

In this research, a neural network (NN) method was developed, which combines H-infinity and fuzzy control for the purpose of stabilization and stability analysis of nonlinear systems. The H-infinity criterion is derived from the Lyapunov fuzzy method, and it is defined as a fuzzy combination of quadratic Lyapunov functions. Based on the stability criterion, the nonlinear system is guaranteed to be stable, so it is transformed to be a linear matrix inequality (LMI) problem. Since the demo active vibration control system to the tuning of the algorithm sequence developed a controller in a manner, it could effectively improve the control performance, by reducing the wind's excitation configuration in response to increase in the cost efficiency, and the control actuator.

• Journal of IKEEE
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• v.13 no.1
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• pp.11-18
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• 2009

In this paper, the stability of second order uncertain systems with regulation of PID type controllers is analyzed by using Lyapunov second method for the first time in the time domain. The property of the stability of PID regulation servo systems is revealed in sense of Lyapunov, i.e., bounded stability due to the disturbances and uncertainties. By means of the results of this stability analysis, the maximum norm bound of the error from the output without variation of the uncertainties and disturbances is determined as a function of the gains of the PID control, which make it enable to analyze the effect resulted from the variations of the disturbances and uncertainties using this norm bound for given PID gains. Using the relationship of the error from the output without variation of the uncertainties and disturbances and the PID gain with maximum bounds of the disturbances and uncertainties, the robust gain design rule is suggested so that the error from the output without the variation of the disturbances and uncertainties can be guaranteed by the prescribed specifications as the advantages of this study. The usefulness of the proposed algorithm is verified through an illustrative example.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4342-4366
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• 2016

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur'e type system. In the context of delayed Lur'e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.156-160
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• 1993

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an inproved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing force coming from the difference between th actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.189-195
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• 2000

The mathematical solutions of the stability convergence are important problems in system control. In this paper such problems are analyzed and resolved for system control using multilayer neural networks. We describe an algorithm to control an unknown nonlinear system with a disturbance, using a multilayer neural network. We include a disturbance among the modeling error, and the weight update rules of multilayer neural network are derived to satisfy Lyapunov stability. The overall control system is based upon the feedback linearization method. The weights of the neural network used to approximate a nonlinear function are updated by rules derived in this paper . The proposed control algorithm is verified through computer simulation. That is as the weights of neural network are updated at every sampling time, we show that the output error become finite within a relatively short time.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.142-147
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• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2190-2192
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• 2003

In this paper, a state observer based direct adaptive fuzzy controller for unknown nonlinear dynamical system is presented. The adaptive parameters of the direct adaptive fuzzy controller can be tuned by using a projection algorithm on-line based on the Lyapunov synthesis approach. A maximum control is used to guarantee the robustness of system. A stability analysis of the overall adaptive scheme is discussed based on the sense of Lyapunov. The inverted pendulum simulation example shows that proposed control algorithm can be used for the tracking problem of nonlinear system.

• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.877-885
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• 2018

Various controllers such as proportional-integral-derivative (PID) controllers have been designed and optimized for load-following issues in nuclear reactors. To achieve high performance, gain tuning is of great importance in PID controllers. In this work, gains of a PID controller are optimized for power-level control of a typical pressurized water reactor using particle swarm optimization (PSO) algorithm. The point kinetic is used as a reactor power model. In PSO, the objective (cost) function defined by decision variables including overshoot, settling time, and stabilization time (stability condition) must be minimized (optimized). Stability condition is guaranteed by Lyapunov synthesis. The simulation results demonstrated good stability and high performance of the closed-loop PSO-PID controller to response power demand.

• Structural Engineering and Mechanics
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• v.58 no.6
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• pp.1127-1143
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• 2016

The pounding phenomenon in adjacent structures happens in severing earthquakes that can cause great damages. Connecting neighboring structures with active and semi-active control devices is an effective method to avoid mutual colliding between neighboring buildings. One of the most important issues in control systems is applying online control force. There will be a time delay if the prose of producing control force does not perform on time. This paper proposed a time-delay compensation method in coupled structures control, with semi-active Magnetorheological (MR) damper. This method based on Newmark's integration is adopted to mitigate the time-delay effect. In this study, Lyapunov's direct approach is employed to compute demanded voltage for MR dampers. Using Lyapunov's direct algorithm guarantees the system stability to design a controller based on feedback. Because of the strong nonlinearity of MR dampers, the equation of motion of coupled structures becomes an involved equation, and it is impossible to solve it with the common time step methods. In present paper modified Newmark-Beta integration based on the instantaneous optimal control algorithm, used to solve the involved equation. In this method, the response of a coupled system estimated base on optimal control force. Two MDOF structures with different degrees of freedom are finally considered as a numeric example. The numerical results show, the Newmark compensation is an efficient method to decrease the negative effect of time delay in coupled systems; furthermore, instantaneous optimal control algorithm can estimate the response of structures suitable.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.256-270
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• 2023

In this study, a sliding mode (SM) controller for dynamic positioning (DP) was specifically designed for a turret connection operation of a ship or an offshore structure in which an arbitrary point on the structure could be controlled as the motion center instead of the center of mass. The SM controller allows control of the arbitrary point and provides capability to manage uncertainties in the dynamics of ships and offshore structures, external forces caused by unknown changing marine environments, and transient performance of DP systems. The Jacobian matrix included in kinematic equations of the controlled object was modified to design the SM controller to control based on an arbitrary point of ships or offshore structures. To ensure robustness of the controller, the Lyapunov stability theory was applied in the design of the SM controller. In general, for robustness in DP control, gain scheduling based on a proportional-derivative (PD) control algorithm is employed. However, finding appropriate gains for gain scheduling complicates the application of DP systems. Therefore, in this study, the SM control algorithm was considered to mitigate the complexity of the DP controller for ships and offshore structures. To validate the proposed SM control algorithm, time-domain simulations were conducted and utilized to evaluate the performance of the control algorithm. The effectiveness of the proposed SM controller was assessed by comparing simulation results with results of a conventional PD control algorithm applied in DP control.