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In this paper, a new approach for controller designing of robust output feedback model reference adaptive control with a function of tracking error for a class of continuous linear systems with multiple uncertain time varying state delays is introduced. Proposed controller is not only robust versus of multiple uncertain time delays and external disturbance with uncertain bound but also improve performance of transient and steady state of closed loop system. The stability of closed loop system and error convergence is shown with an appropriate Lyapunov-Krasovskii function. Simulation results show good performance of proposed controller.

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In this paper a novel method is introduced for target detection in bistatic passive radars which uses the concept of correntropy to distinguish correct targets from false detections. In proposed method the history of each cell of ambiguity function is modeled as a stochastic process. Then the stochastic processes consist the noise are differentiated from those consisting targets by constructing an FIR adaptive filter. A cost function which is based on correntropy is utilized to update the filter. The performance of the proposed method is evaluated by simulation in presence of rapid and slow moving targets. The obtained results shows the superiority of the proposed method compared to its alternatives in such manner that it detects rapid targets at least 18.7 and 20.1 percent better than HOSCM and PFCM. Furthermore it detects slow targets 19.3 and 21.4 percent better than those alternatives, all in presence of maximum noise (i.e. SCNR=-30dB). 

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In this paper, a fractional-order robust adaptive intelligent controller (FRAIC) is designed for a class of chaotic fractional order systems with uncertainty, external disturbances and unknown time-varying input time delay. The time delay is considered both constant and time varying. Due to changes in the equilibrium point, adaptive control is used to update the system's momentary information and the intelligent controller is used to estimate the uncertainties and disturbances and non-linearities of the system according to the momentary information obtained. The sliding mode control, which provides closed loop asymptotic stability in the system despite the uncertainties and disturbances, is used as a robust controller. Using the Lyapunov theorem and Barbalat's Lemma, the asymptotic stability of a chaotic fractional order system with input delay and uncertainty as well as external disturbance is proved by designed controller. Finally, using the simulation results of financial as well as supply and demand systems, the performance of designed controller would be examined.

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The paper presents an optimal and coordinated power oscillation damper based on a wind turbine and power system stabilizer (PSS) to maintain the power system stability and damp inter-area oscillations. The optimal and coordinated design of the PSS located at the generator site and the damper which was installed in the control section of the doubly-fed induction generator (DFIG) is defined as an optimization problem and simulations have been performed in MATLAB software environment. To determine optimal coefficients of the PSS and damper, the metaheuristic salp swarm optimization (SSA) algorithm was employed with an objective function that aimed to minimize the error caused by frequency deviations of two areas. Due to the use of wide-area measurement systems (WAMS) in the proposed damper to enhance controllability and observability of most of the oscillation modes, time delays resultant from the WAMS was also taken into account. Additionally, uncertainties of wind intermittency and time delay of WAMS were calculated probabilistically. The suggested method was applied to a six-machine two-area power system with a wind farm. The obtained simulation results highlighted and validated the superior performance and stability of the power system as a result of using the proposed method.