Journal of Control
Year:2018 | Volume:12 | Issue:1 |Papers Count:5

In this paper tracking control law design for a class of polynomial fuzzy systems is considered. The control law consists of an observer and a state feedback. A polynomial fuzzy observer estimates the state vector of the plant, and then the estimated state vector is employed by a polynomial feedback gain to fulfill the control law. The polynomial fuzzy control law leads the state vector of the plant to track the state vector of a stable reference model subject to an performance. Sufficient conditions for determination of the control law parameters will be presented in the form of an SOS program. Additionally simulation results are presented to show the merits of the proposed control design approach.

This article studies dynamical systems with one-sided Lipchitz nonlinear functions in the presence of time-delay and unknown terms due to model uncertainties and external disturbances. The one-sided Lipchitz condition is less conservatism with respect to well-known Lipchitz condition and includes a wider class of nonlinear functions. The goal of this paper is design of state feedback controller for the considered system which guarantees the robust and finite time stability of the state variables of the closed-loop system. For this purpose, based on the Lyapunov approach in stability analysis of time-delay systems; the appropriate Lyapunov-Krasovskii functional is selected and the sufficient conditions for robust finite-time stabilization are given based on linear matrix inequalities. The feedback gain is also calculated by solving the obtained matrix inequalities. Finally, numerical examples and simulations are given to show the performance of the proposed method. Additionally, it is shown that the proposed theorem has been less conservative and its functional range is wider.

Magnetometer is one of the most important sensors used in the satellite attitude determination and control system. Due to occurrence of various errors when the satellite is separated from the launcher and also during its rotation in the orbit, it is necessary to re-adjust onboard the sensor parameters. For this purpose, some solutions are proposed in this paper in which the satellite current attitude is not required. In this regard, first a magnetometer model is presented that despite conventional models; it includes nonlinearity, hysteresis and data quantization effects, permeability and installation error. Then, for sensor onboard calibration purposes, two stages-offline and two-stage online series structures are suggested. In the offline case, the centered solution and Levenberg Marquardt methods have been integrated. Also, the extended and unscented Kalman filters are integrated for online case. Utilizing the suggested algorithms, different errors including bias, scale factor and installation errors are simultaneously determined and also the accuracy is improved compared to the similar works. The simulation results for a Leo satellite show that the sensor parameters are derived with acceptable accuracy. Accordingly, it will be illustrated that the centered solution method has lower computational load and shorter time convergence, but it has lower accuracy with respect to online methodology.

Vision-based robot control is a method to motion control of a robot using information extracted from visual sensors. In traditional approaches, a model of robot and camera are needed. Obtaining these models are time consuming and sometimes impossible. Recently, intelligent methods are used to cope the above challenges. In this paper, a hybrid fuzzy controller is proposed to control a robot manipulator. Visual inputs of the controller are provided by Kinect and outputs are the rotation of joints motors. The hybrid controller contains two controllers. The first controller in based on fuzzy inverse model which approximates real inverse model of robot using gathered data. In order to increase accuracy, a fuzzy expert controller is designed and it is used when the end-effector is in the predefined near-goal area. Since determining exact value of the fuzzy expert controller parameters is impossible, in addition to make system adaptive with small changes in the environment, actor-critic architecture is used. This architecture is a well known continuous reinforcement learning methods. The proposed method is applied to control a real robot manipulator (ARM_6AX18). Experimental results show that using the proposed method in practice, the end-effector reaches from any random start position to the goal position with a good accuracy in robot workspace.

In this paper, two main subjects are dealt with: stabilization of supercavitating vehicles in depth mode and estimating the state variables of the vehicle in order to control the vehicle in this mode. Using the feedback linearization method, the model of the system is linearized and a linear quadratic regulator is designed for the system to stabilize it. This method needs to feedback all states of the system, while measuring all the states is practically infeasible. Then, it is needed to estimate some of the states using the model of the system and the sensor measurements. This is performed here using two well-known filters of EKF and UKF. Through simulations, it is shown that both filters can estimate the states of the system in the depth mode, stabilize the vehicle in this mode and reject the disturbances. It is observed that each filter can estimate some of the states more accurately. In simulations, the performances of the designed controllers are examined, practical issues like actuator saturation are taken into account and the ability of the controllers to stabilize the vehicle is demonstrated.