Journal of Control
Year:2015 | Volume:9 | Issue:2|Papers Count:6

Stabilization of switched linear systems is one of the most important problems in the field of switched systems. On the other hand, asynchronous switching between the controller and the system may lead to an unstable closed loop system or degrade the closed loop system’s performance. In this paper the problem of stabilizing a switched system with both stabilizable and unstabilizable subsystems and asynchronous switching between the controller and the system is considered. Using Lyapunov-like functions, some criteria are presented to guarantee the stability of the switched system based on the activity time of the stable and unstable subsystems and maximum number of switchings in a given time interval for both discrete-time and continuous-time cases. The last issue, verified in this paper is the design of a state feedback controller which can provide stability criteria, as well as designer’s desired criteria, including minimum activity time of the stable subsystems. In order to illustrate the effectiveness of the proposed method, simulation results are presented at the end of the paper.

Several algorithms are developed for speed estimation and sensorless control of induction machines which among them, reduced-order based methods have proved acceptable performance. In this paper, a reduced-order speed estimation method, is introduced for sensorless control of DFIGs, in which, rotor currents and stator fluxes are used as state variables and dynamic of rotor currents error is applied to extract adaptation mechanism. Since current dynamic is faster than flux dynamic, proposed observer dynamic is improved. Dynamic stability of proposed method is demonstrated using Lyapunov stability criteria. The suggested speed estimation algorithm is confirmed utilizing a laboratory setup, in addition to simulations. Laboratory setup is based on a 16-bit DSPIC and practical results are extracted using a 250 watts induction machine. Both simulation and practical results show effective and stable operation of proposed method, especially at speed tracking.

In this paper, different cases of the stability of Multiple Adaptive Notch Filter (MANF) are studied. The structure of MANF is composed of N parallel subfilters. Each subfilter estimates the parameters of one the components of quasi periodic signals including the sum of N periodic signals. For this structure, there are three different cases of stability for N=K, N>K and N<K, which include the exponential stability in the isolated equilibrium point, the semistability and the ultimate boundedness in the presence of disturbance. Among these cases, the second and the third cases are analyzed more specifically in this paper and therefore in this paper, in addition to the presentation of MANF, a new approach is proposed to prove the semistability based on the Lyapunov function definition. Also, according to the fact that the estimated frequency of subfiltersincludes a bias, a general form is obtained to determine the estimated frequency of subfilters in the case of the ultimate boundedness under disturbance. Additionally, in order to cancel this bias, a method is proposed based on the use of rectangular window functions in MANF. Simulations are carried out to demonstrate that using the rectangular window enhances the ANF performance.

In this study, a hierarchical fuzzy controller associated with PD classical controller with feedback error learning method for class of canonical SISO nonlinear system in presence of bounded disturbance is presented. The stability of whole system is guaranteed through a Lyapunov function. The adaptation laws of all parameters of consequent part of hierarchical fuzzy system are derived using it. Tunable parameters of hierarchical fuzzy system are appeared in nonlinear form at the output. Using the mentioned theorem, they are appeared in linear form. There is an upper bound for the residual terms. They are considered in Lyapunov function and the adaptation law is derived for them. It is shown that, the proposed theorem is applicable to the hierarchical fuzzy system with any structure and any number of layers and even to the ordinary fuzzy systems. Also, using hierarchical fuzzy controllers leads to reduction of number of rules and parameters in a fuzzy system. Finally, the proposed method is applied on two systems (flexible joint robot and quarter active suspension system). The results are compared with classical sliding mode method. They reveal the efficiency of the suggested algorithm.

Automatic traffic regulation plays an important role in public transportation especially in metro lines. Delay recovery and real-time control strategies are employed to obtain optimal schedule of high frequency lines in recent years. This paper presents a new traffic model for metro loop lines based on the time deviations of departure times. In this model, a phenomena of transferring knock on delay from one train to another one has been considered. The objective function is considered to minimize the time deviation and increase the passenger satisfaction with headway adherence. Nonlinear model predictive controller is employed to compensate the disturbances and recover the nominal time schedule in metro traffic system by varying the running time between two successive platforms. We minimize the objective function by designing nonlinear model predictive controller in the presence of the operational constraints on control actions and time interval between two successive trains. Simulation results verify the introduced model and show the performance of nonlinear model predictive controller to minimize the delays.

Unfalsified Adaptive Control (UAC) is a recently proposed robust adaptive control strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-Model UAC is followed as an intermediate between UAC and multiple model control. Different approaches in UAC and MMUAC are studied. Also, Multi-Model Unfalsified Generalized Predictive control (MMUGPC) is proposed, which is a new control design strategy in the UAC framework. For an uncertain system, by utilizing several generalized predictive controllers and discrete switching between them with unfalsified control, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-Model Unfalsified Generalized Predictive control.