Journal of Control
Year:2016 | Volume:10 | Issue:2|Papers Count:7

A model free terminal sliding mode controller has been proposed. The proposed controller is data driven i.e. based only on input and output data (thus, model free). This method employs a recursive nonlinear sliding surface. This leads to higher tracking precision and to finite time convergence of the response. The method uses “Model Free Adaptive Controller” approach, combined with “Discrete time Sliding Mode Controller” method. Boundedness of tracking error is proved analytically. Theoretical analysis also shows the superiority of the proposed method over Model Free Linear Sliding Mode Controller. Analysis results are confirmed via simulation.

In this paper, a new approach is proposed for stability analysis of fractional stochastic systems. By extending the concept of infinitesimal operator using the fractional Ito formula, it becomes possible to apply it in fractional stochastic systems for stability analysis by Lyapunov functions. Thereafter, the presented stability criterion is utilized to develop the sliding mode control scheme for fractional stochastic systems with state delay. The proposed design method ensures that the state trajectories reach the sliding surface with probability one. Stability analysis of the system at sliding mode is executed using the given fractional infinitesimal operator and the stochastic stability conditions are given in the form of linear matrix inequalities. To illustrate the efficiency of the results, the application of the method is presented for the pitch control of a variable speed wind turbine.

A nonlinear adaptive control algorithm for a multi-task redundant manipulator is developed in this paper. The method considers the parametric uncertainties in the system and defines a proper filtered error signal according to the allocated priority. Based on this error analysis, the asymptotic stability and convergence of the tracking error, both for the main task as well as the sub-tasks are shown using Lyapunov approach. In order to extend the algorithm for the case of orientation control in the operational task, quaternion feedback has been exploited, and the stability is shown. The results of the paper are verified in several simulations on 4DoF planar arm as well as 7DoF KUKA lightweight robot arm.

In this paper, a numerical method for solving stochastic optimal control problem by using Markov chain approximation method has presented. The basic idea of the Markov chain approximation method is to approximate the original controlled process by an appropriate controlled Markov chain on a finite state space.Also, we need to approximate the original cost function by one which is appropriate for the approximating chain. These approximations should be chosen such that a good numerical approximation to the associated optimal control problem can be obtained, which means the conditional mean and covariance of the changes in state of the chain are proportional to the local mean drift and covariance for the original process. The finite difference approximations are used to the construction of locally consistent approximating Markov chain, the coefficients of the resulting discrete equation can serve as the desired transition probabilities and interpolation interval.

Environmental surveillance and recognition are important issues to avoid surprises and conditions control. This awareness is necessary to combat and prevent the enemy attacks and increase deterrence. Direction of Arrival (DoA) estimation and localization targets are including the main bases environmental surveillance in an environment. DoA acoustic means calculated to enter acoustic wave emitted by the source in sensors that is provided different methods such as MVB, MUSIC and TDoA for this issue. The proposed method with simplification of trigonometric equations calculate the location is provided new equations to estimate direction of arrival acoustic signal for hemispherical environment that number equal to twodimensional state. DoA estimation for hemisphere environment is provided for this reason, often with installed DoA estimation system on a platform is needed to be half of environment localization or DoA estimation and also just by adding a sensor, system will obtain the ability to work in sphere environment. In this simplification is decreases the computational complexity and the number of sensors needed to DoA estimation a hemisphere.In the proposed method is obtained the responses with acceptable error for elevation and azimuthal angles in simulations.

This paper proposes a recursive method to determine the process and measurement noise covariance matrix in the extended Kalman filter in application of bearing-only target tracking.One of the requirements of Kalman filters is knowledge of process and measurement noise covariance matrices. If the inappropriate choice of covariance, the filter performance is affected and even there is the possibility of divergence. In this paper, a recursive structure to adapting noise covariance is presented that unlike the conventional methods, instead of direct adapting covariance matrices, based on steepest descent adapting rule structure parameters are adapted.This increases the reliability of the adaptive method andnon-negative condition of some of covariance matrix elements to be resolved. To evaluate the performance of proposed method, the bearing-only target tracking scenario is considered. To compare the proposed approach, three adaptive covariance common methods is used that simulation results show that the reliability and efficiency of the proposed method.