Journal of Control
Year:2012 | Volume:6 | Issue:2|Papers Count:7

In this paper, a model-based active fault tolerant control system (FTCs) is proposed for three phase induction motor (IM) drives subjected to the mechanical faults caused by both stator and rotor failures. FTCs structure consists of two main parts. The first part is a nominal controller based on feedback linearization for fault-free case to achieve control objectives (rotor flux and speed control). The second part is a sliding mode observer (SMO) in order to estimate additive faults which model mechanical faults in the state space model of IM. This observer has been used not only for fault reconstruction and production of additional control inputs for compensating their undesirable influences on performance of IM, but also for online estimation of axial fluxes in any operating conditions. The simulations results are shown to illustrate the effectiveness of the proposed approach to compensate the mechanical faults in IM.

Non-Dimensional star pattern recognition is a novel algorithm which an overall system error analysis in order to determine its robustness is not performed for it so far. Although the mentioned algorithm is independent of Focal length and optical offset variations, its operational usage is still bounded. In this research, algorithm’s robustness is determined using an overall error effect of bright point coordinate variations. The results demonstrate that the non-dimensional algorithm performance is pretty fragile in the presence of a common 0.1 pixel error, which makes the algorithm unable to perform onboard Nasir 1 star tracker. Eventually the algorithm was modified and the results show great improvements.

Analysis of the inverse kinematics of redundant manipulators is one of the nesseccary tools in various robotic fields such as design, motion planning and control of these systems. Since, there is not an analytical solution for the inverse kinematics of several redundant manipulators, numerical approaches are needed to execute and investigate in this field. In this paper, combination of the neural networks, fuzzy systems and quadratic programming is used to obtain the joint variables. According to the proposed approach, seven neural networks are considered according to the each joint variable and by adaptation of the neural network’s weights, suitable configurations of the robot is determined to track a desired trajectory in the Cartesian space. Meanwhile, initial weights of the neural networks are obtained by fuzzy systems based on the vicinity of the endeffector to desired point and feasibility of the joint variables. Obstacle avoidance scheme is performed by investigation of the conditions including choosing the joint variables that involved in the equations of the arms constraints and determination of the most critical arm. In order to establish the constraints of the problem in the quadratic programming, realization of the Kun-Tucker conditions will be used. Evaluation of the proposed approach will be carried out on the PA-10 manipulator by simulation and analysis of the results.

Cooperation of man and machine is one of the best solutions for moving the endeffector of a manipulator on a specific path with unknown relation. In such a solution deviation from the path is assessed by operator and necessary correcting command is generated by him based on that. This navigation command is, then, used by control algorithm of manipulator to move the end-effector on the desired path. This problem gets more difficult if the base of manipulator is moving on a platform such as a cart or a large manipulator, with inherent flexibility, to achieve a large workspace. In such cases it is impossible to use a master similar to the manipulator itself. This paper presents a fuzzy navigation algorithm for moving end-effector of a manipulator mounted on a moving base. The algorithm which is based on estimation of cross track error of end-effector, generates necessary command which is used by a proposed computed-torque method algorithm to move the end-effector on the desired path. Simulation results shows versatility of the proposed methods for navigation and control of the system even in presence of disturbances due to flexibility of base.

In this paper, a new guidance law is designed for missile against maneuvering target by integrating optimal control SDRE technique and sliding-mode control. Due to the fact that autopilot dynamic has a very important role in success or unsuccess of engagement in terminal phase, and it can make delay in guidance commands execution, this dynamic is taken into account in state equations. The robustness of the designed guidance law against disturbances is proved by the second method of Lyapunov. The proposed guidance law does not need accurate target maneuver profile and just need the maximum value of the target maneuver. Coefficients in proposed guidance law are obtained using genetic algorithm. For investigating effectiveness of proposed guidance law, by considering different scenarios, three-dimensional missile-target engagement is simulated. Then results are compared with conventional augmented proportional navigation guidance (APNG) law. Simulation results show that the proposed guidance law has high robustness against target maneuver disturbances and also one can compromise between convergence speed, intercept time and control effort.

When using game theory for modeling real- world problems, players’ payoffs are usually known approximately. Literature reveals that some authors have modeled the approximate payoffs using stochastic or fuzzy variables and some others have used robust optimization techniques to solve these games. Surprisingly little work has been done on robustness analysis of real- world’s games solutions.In this paper, we propose two simple and practical measures to assess robustness degrees of Nash equilibria. These measures quantitatively show how Nash points behave in the presence of uncertainty and they can be used as refinements of Nash equilibrium. Also we propose two novel approaches to assess robustness degrees of correlated equilibria. One approach is a quantitative way to calculate robustness degrees and the other is a comparative measure to rank correlated equilibria in order of their robustness. We suggest that the decision maker may be able to find more robust solutions in the set of non- Nash correlated equilibria. Moreover, we present a method to improve robustness of Nash points. The improvement algorithm searches for more robust solutions in a neighborhood around a Nash point.We validate our methods with some numerical examples. The examples verify the efficiency of the methods.

In this paper, we propose a new method to design a decentralized reduced order observer for large scale plants with unknown inputs. In this approach, large scale plant is decomposed into several subsystems with interconnected terms, then interconnected terms will be eliminated by using the appropriate transformations in new form of dynamical equation of each subsystem. Based on this method, states estimation doesn’t require exchanging information between the subsystems. Here, if plant satisfies the existence condition for designing stable observer with unknown input (UIO), we use estimation error dynamic and negative definite to provide the observer convergence. Finally, effectiveness of the method is shown by using a numerical example and corresponding simulation.