Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
This paper presents a recent self-sampled-data control algorithm applied to nonlinear\nsystems with actuator failures. Our approach uses the linear model of a given nonlinear system,\nand based on a granted actuator fault observer method, an asynchronous sampled-data fault\ncompensator controller is then formulated. The proposed sampling rule is realized by using an\nevent-detector monitoring signal invention. On this way, the sampled rate is self governed and\nasynchronous by nature. Hence, our contribution is twofold. Fist, a new auto-generated non-uniform\nsampled-data mechanism is stated. Second, we grant an event-triggered control law with actuator\nfailure observation and compensation. Our findings are completely supported by employing\nLyapunov�s theory. Finally, according to our numerical experiments applied to an undamped\ntorsional pendulum, our design is able to detect a failure in the actuator device and it can stabilize the\nundamped torsional pendulum system presenting better performance in comparison to its open-loop\ndeployment....
A novel approach to fault diagnosis for a class of nonlinear uncertain systems with triangular form is proposed in this paper.\nIt is based on the extended state observer (ESO) of the active disturbance rejection controller and linearization of dynamic\ncompensation. Firstly, an ESO is designed to jointly estimate the states and the combination of uncertainty, faults, and nonlinear\nfunction of nonlinear uncertain systems. It can derive the estimation of nonlinear function via the state estimations and system\nmodel. Then, linearization of dynamic compensation is employed to linearize the system by offsetting nonlinear function\nmandatorily using its estimation. An observer-based residual generator is designed on the basis of the prior linearized model for\nfault diagnosis. Moreover, threshold treatment technique is adopted to improve the robustness of fault diagnosis. This method is\nutilizable and simple in construction and parameter tuning. And also we show the construction of ESO and give the corresponding\nconvergence proof succinctly. Finally, a numerical example is presented to illustrate the validity of the proposed fault diagnosis\nscheme....
Networked Control Systems (NCSs) have been implemented in several different\nindustries. The integration with advanced communication networks\nand computing techniques allows for the enhancement of efficiency of industrial\ncontrol systems. Despite all the advantages that NCSs bring to industry,\nthey remain at risk to a spectrum of physical and cyber-attacks. In this paper,\nwe elaborate on security vulnerabilities of NCSs, and examine how these vulnerabilities\nmay be exploited when attacks occur. A general model of NCS designed\nwith three different controllers, i.e. , proportional-integral-derivative\n(PID) controllers, Model Predictive control (MPC) and Emotional Learning\nController (ELC) are studied. Then three different types of attacks are applied\nto evaluate the system performance. For the case study, a networked pacemaker\nsystem using the Zeeman nonlinear heart model (ZHM) as the plant\ncombined with the above-mentioned controllers to test the system performance\nwhen under attacks. The results show that with Emotional Learning\nController (ELC), the pacemaker is able to track the ECG signal with high fidelity\neven under different attack scenarios....
This paper presents a predictive control of omnidirectional mobile robot with three independent driving wheels based on kinematic\nand dynamic models. Two predictive controllers are developed.The first is based on the kinematicmodel and the second is founded\non the dynamic model.The optimal control sequence is obtained by minimizing a quadratic performance criterion. A comparison\nhas been done between the two controllers and simulations have been done to show the effectiveness of the predictive control with\nthe kinematic and the dynamic models....
Renewable energy sources for vehicles have been the motivation of many researches around the world. The reduction of fossil fuels\ndeposits and increase of the pollution in cities bring the need of more efficient and cleaner energy sources. In this way, this work\nwill present the application of a compressed air engine applied to a bicycle. The engine is composed of two pneumatic cylinders\nconnected to the bicyclewheel through a crank-connecting-rod mechanism. In order to control the velocity of the bicycle, a strategy\nof control composed of two controls was implemented: a feedback and a feedforward control. For feedback control, the State-\nDependent Riccati Equation (SDRE) control and also a proportional-derivative (PD) control are considered, considering three\ncases for velocity bicycle variation: 10 km/h, 20 km/h, and 30 km/h. The equations of motion of the system were obtained through\nthe Lagrangian energy method. Numerical simulations were performed in order to analyze the dynamics of the system and the\nefficiency of the controllers....
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