مجله کنترل
سال دهم شماره 1 (بهار 1395)

In this paper, a modified adaptive nonlinear filter (MANF) to estimate the vehicle longitudinal velocity of the vehicle has been presented which has an acceptable accuracy and simple structure. Obtaining accurate model with minimum uncertainty is difficult and almost impossible, consequently, conventional model based filters can not be usded simply in estimating the longituadinal velocity of the vehicle during the braking. On the other hand, the model based filters reuire the braking toque in velocity estimation where, measuring the braking torque requires specific sensors which will cause increasing the costs of repairing and maintenance. Consequently, in this paper a MANF has been introduced which didn’t require the dynamic model of vehicle and the value of braking torque in estimation process. MANF has been evaluated by experimental data and its effectiveness has been approved.

In this paper, the effect of the wind turbine blade pitch fault and fault detection method based on support vector machine algorithm based on electrical and mechanical signals have been modeled and studied. Dynamic model of permanent magnet synchronous generator and wind turbine under both aerodynamic asymmetry and normal conditions modes have been modelled in Simulink, FAST and TurbSim environment. The aerodynamic asymmetry has been simulated by adjusting the pitch of one blade different from the other pitch blades. . The simulation results recorded in the time-domain and then transformed into the frequency-domain by using the Fast Fourier Transform (FFT). This results shows when the blade failure occurred, the amplitude of excitation frequency 1p is appeared in electric signals and with more intensity in mechanic signals. For fault detection, first the time-domain and frequency-domain parameters of signals extracted, and then, The sensitivity of this parameters in healthy and faulty conditions obtained by using Distance Evaluation Criteria (DEC). The result of DEC considered as a variable input in SVM. This process implemented for the number of 60 wind Turbulence for healthy and faulty turbines. Simulation results confirmed that the proposed approach to be able to identifying the healthy condition from aerodynamic asymmetry fault in wind turbine and this proposed approach is effective and efficient for blade fault detection.

Congestion is one of the most well-known problems in data networks. The congestion occurres when the number of packets being transmitted through the network exceeds the packet handling capacity of the network. The aim of AQM is to keep the queue size at a certain desired level at which network performance falls off dramatically. This situation leads to long time delays, and may even result in instability in the network. The purpose of this paper is to design controller for congestion control of the TCP network using Finite Spectrum Assignment (FSA) technique and comparison with other conventional methods including Proportional-Integrator (PI), Random Early Detection (REM) and H∞. To evaluate the performance of the proposed controller, different experiments are conducted through NS-2 simulations by considering variations in network dynamics.

In this study, an observer based indirect adaptive fuzzy controller for the anti-lock braking system (ABS) is proposed. First, using the Lugre internal friction model in the single corner model of automobile, road profile is estimated. Then, using the estimator results, the optimum slip is estimated. It should be noted that in the optimum slip, the maximum longitudinal friction created between the tire and road is achieved, so that the automobile stops in the least possible distance. Since some of state variables of the system are difficult to measure or contaminated by noise, and observer based controller is designed in this paper. The controller objective is to realize the reference slip obtained from the road condition estimator. Using Lyapunov stability theory, it is verified that the values obtained for road condition and vehicle speed will converge to their actual values. In addition, it is shown that the indirect adaptive fuzzy controller results in asymptotic tracking of the optimum slip. Simulations results show the proper performance of the proposed anti-lock breaking control system in achieving the optimum slip and quick stop of the car without locking the wheel in various road conditions.

This paper presents a novel hybrid technique based on the modal series method and linear programming strategy for solving the optimal control problem of nonlinear fractional-order systems. The fractional derivative is defined in the sense of Riemann-Liouville with order less than one. The performance index includes the terminal cost in addition to the integral quadratic cost functional. Both the fixed and free final states cases have been taken into account. In this approach, first we extend the modal series method in order to convert the original nonlinear fractional-order two point boundary value problem (FTPBVP) derived from the Pontryagin’s maximum principle into a sequence of linear time-invariant FTPBVPs. This sequence is then transformed into a sequence of linear programming problems by defining a new variational problem in the calculus of variations, using a discretization technique based on the first-order Grünwald-Letnikov approximation and introducing a new transformation. The convergence analysis of the proposed approach is also provided. To achieve an accurate suboptimal control, we apply a fast iterative algorithm with low computational effort. Finally, two numerical examples are included to illustrate the effectiveness of the proposed approach.

Diabetes is one of the important issues in the medical field and determination of appropriate rate of insulin injection in order to regulation of blood glucose level is vital for diabetics. In this thesis the problem of tracking of desired plasma blood glucose by using subcutaneous insulin administration is expressed. Mathematical model is used in this thesis for glucose-insulin regulation system was based on Palumbo delayed nonlinear model and is one of the most comprehensive models in recent years. The control method is used by Palumbo based on feedback linearization is one of the most complete control methods in this field. The main problem of this method was locus location of the desired closed-loop poles issue based on trial and error. Location of closed-loop poles away from imaginary axis can cause reduce of oscillations and increase of steady state error that in insulin injection issue is considered a fundamental problem. To fix this problem, in this paper for the first time employing combination of the fuzzy logic controller optimized by genetic algorithm with feedback linearization method as hybrid method is used to regulate the blood glucose level in diabetic patients and stabilization of blood glucose level three diabetic patients with different initial glucose level is simulated. One of the advantages of the proposed method is very high robustness controller against the uncertainty and disturbance. Finally, the simulation results of proposed control methods are compared with the control method based on Palumbo poles location, which indicates the phenomenal functioning of the proposed controller for tracking of desired blood glucose level at the lowest possible error in the presence of uncertainty or without the uncertainty.