مجله کنترل
سال دهم شماره 4 (زمستان 1395)

This paper presents a new method for designing a generalized delay timer based on penalty scenario and Markov chain schemes. The penalty approach is the extension of n-sample on/off delay approaches in designing alarm systems. Three performance indices named, False Alarm Rate (FAR), Missed Alarm Rate (MAR) and Average Alarm Delay (AAD) are derived for penalty approaches using Markov models. Also, the new index named “Mean Time to Alarm (MTTA)” is introduced to analysis alarm system and computation of AAD in penalty scenario. The optimal values of delays and penalties are derived based on target function using Genetic Algorithm (GA). Finally, the effectiveness of the proposed method is investigated and compared with other methods through a case study.

In this paper, we study the containment control problem of heterogeneous linear multi-agent systems under directed topology based on output regulation framework and using output feedback control. Motivated by leader-follower output regulation problems, the leaders are assumed to be exosystems with identical dynamics. In controller design approach for each follower, we utilize a distributed dynamic output feedback control scheme. To achieve the objective of this work we utilize the containment error, which enables us to consider more than one leader in output regulation framework and to use output regulation techniques with some modifications to solve the containment problem. Finally, the validity of theoretical results are demonstrated through an example.

Stability is one of the most important issues in swarm movement. Stability in swarm movement is composed of features such as collision avoidance between members of the swarm with each other and also obstacle avoidance along the path of movement. This paper presents a new algorithm in the form of a dynamic equation based on Lagrange equations for stable movement of the swarm robot. One of the objective of presenting this algorithm is to enable aggregation at the target point. The new algorithm will be extended in two cases; first, when the members of the swarm have the tendency for aggregation from the beginning of the movement, and second, members of the swarm don’t have the tendency for aggregation along the path. At the end, the presented algorithms will be simulated and evaluated. The simulation results show successful obstacle avoidance along the path and aggregation at the target point. The results show that in the second case—when there is no aggregation along the path—the members of the swarm cover more area along the path. Covering more area along the path empowers the application of the proposed algorithm in cases such as identification, exploration, environmental monitoring and mapping along the path.

Chaos is an unpredictable phenomenon that is considered in the nonlinear dynamical systems. In this paper, the chaos control in the nonlinear dynamic gear transmission system is considered. At first, the complex dynamics characters of gear transmission system are studied. Then, the existence of chaos in this nonlinear dynamic system is studied by phase portrait. The design process of sliding-mode control is divided into two steps: The first step is designing a sliding surface so that the plant restricted to the sliding surface has a desired system response, and the second step is constructing a switched feedback gains necessary to drive the plant’s state trajectory to the sliding surface. The stability of the proposed sliding surface is studied by the first theorem in this paper. Chaos control and stabilization of the gear transmission system states is studied based on the Lyapunov stability theorem. Also the stability of the closed loop system is investigated. The effects of the unknown controller parameters, system uncertainty, external disturbance and nonlinearity in the control input are fully taken into account. Appropriate adaptation laws are obtained to undertake the unknown parameters of the controller. Finally, simulation results demonstrate the feasibility and robustness of the proposed controller.

This paper presents a fractional order Kalman filter for the stochastic nonlinear fractional order systems where the measurement noise is assumed to have Student’s t distribution. Modeling of the system and measurement noises in the state space representation, for target tracking problems, often carried out by additive Gaussian noise, but Gaussian distribution is not enough robust to the outliers, in the other words, Gaussian distribution has much less robustness against outliers than Student's t distribution, so if the tracking system affected by noises that have outliers, modeling of theses noises by the Gaussian distribution, may cause large estimation error or divergence of filter in the filtering and estimation problems. In this paper, after defining the stochastic fractional order systems, by comparing the performance of two Student’s t and Gaussian distributions in the modeling of noises that have outliers, try to design a nonlinear Kalman filter for fractional order systems using variational Bayesian inference method. Finally by comparing the simulation results of proposed filter in this paper and a fractional extended Kalman filer in the presence of Gaussian and Student’s t noises, the effectiveness of the proposed filter in estimation of states of the fractional order systems is demonestrated.

In this paper, based on the state-dependent Riccati equation (SDRE) technique, two nonlinear suboptimal controllers (a regulator and a tracker) are designed for a wind turbine with doubly fed induction generator (DFIG). The control objectives include increasing the system stability margin and setting of the electromagnetic torque and the stator reactive power signals to the desired values. In the designed procedure of the controller, a nonlinear model is used for modeling the dynamical properties of the considered DFIG. The performance of the designed controllers to improve the system stability is evaluated in MATLAB software through some simulations for different working conditions of the wind turbine. In addition, an eigen-analysis of the closed-loop system is carried out to evaluate the effects of the proposed SDRE controller on the system stability. The performance of the designed SDRE tracking controller is also investigated through some simulations. In closing, simulation results demonstrate the effectiveness and robustness of the proposed SDRE controllers.