مجله کنترل
سال یازدهم شماره 2 (تابستان 1396)

In this paper the multiple model control of linear time invariant systems with wide uncertainty is studied and a new straightforward and systematic method is proposed to select the local models. The gap metric is used to measure the distance between local models and the maximum stability margin is employed to grid the uncertainty space and measure the permissible distance between local models. The proposed method guarantees the improvement of the maximum stability margin which has direct influence on the reduction of the number of local models and computational complexity loads. To evaluate performance of the proposed method, control of a mass-spring-dashpot system is considered and it is shown that based on our algorithm only a single local model is adequate to control this system while the existing methods in the literature require five local models.

The daily increase of a number of vehicles in big cities poses a serious challenge to efficient traffic control. The suitable approach for optimum traffic control should be adaptive in order to successfully content with the urban traffic that has the dynamic and complex nature. Within such a context, the major focus of this research is developing a method for adaptive and distributed traffic signal control based on reinforcement learning (RL). RL as a promising approach for generating, evaluating, and improving traffic signal decision-making solutions is beneficial and synergetic. RL-embedded traffic signal controller has the capability to learn through experience by dynamically interacting with the traffic environment in order to reach its goals. Traffic signal control often requires dealing with continuous state defined by means of continuous variables. Conventional RL methods do not scale well to problems with continuous state space or very large state space because they require storing distinct estimations of each state value in lookup tables. The contribution of the present research is developing adaptive traffic signal controllers based on continuous state RL for handling the big state space challenge arises in traffic control. The performance of the proposed method is compared with Q-learning and actor-critic and the results reveal that the proposed method outperforms others.

This paper is concerned with the stability of nonlinear polynomial dynamic systems subject to system noise when transmission from sensor to controller is via the digital noiseless channel. A stabilizing technique consisting of an encoder, decoder and a controller is presented for almost sure asymptotic bounded stability of nonlinear polynomial systems subject to system noise over the digital noiseless channel. In the absence of system noise it is shown that the proposed stabilizing technique results in asymptotic stability. The satisfactory performance of the proposed technique for almost sure asymptotic bounded stability and asymptotic stability of a polynomial dynamic system over the digital noiseless channel is illustrated using computer simulations.

This paper presents design and control of a coreless axial flux permanent magnet synchronous generator for variable speed wind turbine application. The effect of design main parameters variation on the active material cost of the generator and its performance characteristics are investigated using sensitivity analysis, and the proper values of design parameters are chosen. The generator is modeled with 3-D Finite Element Method (FEM) and the validity of the design is evaluated. In the control method, the optimum torque values for different wind speeds are calculated to extract the maximum power from the variable speed wind turbine. In order to accurate modeling of the system and direct connection of the generator model in FEM to control system in Matlab-Simulink, the simplorer software is used. Using this software and the real model of the generator in FEM, are lead to more accurate results. The results show that the control system tracks the generator maximum power point with good dynamic response.

In this paper, the trajectory tracking control of underactuated autonomous underwater vehicle without measuring velocity in three-dimensional space and in the presence of unknown disturbances caused by waves and ocean currents is studied based on dynamic surface control for the first time. In order to estimate parametric uncertainties with underwater vehicle dynamic model, radial basis function neural network approximation technique has been proposed. Also, the output feedback control problem is resolved by employing a high-gain observer to estimate the required unmeasurable states. The stability of the proposed controller is investigated by an analysis based on Lyapunov theory and uniform ultimate boundedness stability of states and convergence of tracking errors to a small bound around the origin are guaranteed. Finally, the tracking performance of the proposed control scheme has been verified via computer simulations.

A four-path ultrasonic flow meter was designed and fabricated by using of ultrasonic transmitter/receivers on an 8-inch pipe. By passing fluid through the flowmeter at a controlled rate, the velocity was measured in different layers using the time-difference method. Fluid flow rate was estimated by Gauss-Legendre numerical integration method and applying of the engineering approximation. By comparison of the results with the actual measurement done by a calibrated reference flow meter, the accuracy of the system was tested at various flow rates. It also was compared with the measurement of an equivalent one-path ultrasonic flowmeter, and the measurement errors were calculated. The test result represented that, the average error of measurement is less than 3% at the speeds of 0.5 m/s and more. The average error was more than 15% at the speeds below 0.3 m/s. The measurement error of the one-path flowmeter was 20% more than that of four path one.