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In this paper, a combination of PID controller and linear quadratic regulator is proposed. A fuzzy switching module is applied to optimally fuse both controllers. A new adaptive version of charged system search algorithm optimizes the membership functions of the fuzzy module. By the time, the algorithm changes itself to find a proper solution faster. To show the efficiency of the designed intelligent controller, the results of a simulated unicycle robot under disturbances are presented. [ABSTRACT FROM AUTHOR]

This paper deals with the image-based control of a satellite for remote sensing. Approach is demonstrated by simulation where the position of the satellite is obtained with the Simplified General Perturbations Version 3 model and its orientation by simulating its dynamic and kinematic models. For a known position and orientation of the satellite the images are obtained using the satellite's onboard camera, simulated by the Google Earth application. The orientation of the satellite is governed by reaction wheels, which produce the required moments to the satellite. The image-based control law using SIFT image features is applied to achieve an automatic reference-point observation on the Earth's surface. Main contributions of the paper are the following: use of the same sensor for Earth observation and attitude control, simplicity of the approach, no need for explicit calibration of camera parameters and good tracking accuracy. Demonstrated simulation results and performance analysis confirm the approach applicability. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of mobile-robot localization in structured environments. The extended Kalman filter (EKF) is used to localize the four-wheeled mobile robot equipped with encoders for the wheels and a laser-range-finder (LRF) sensor. The LRF is used to scan the environment, which is described with line segments. A prediction step is performed by simulating the kinematic model of the robot. In the input noise covariance matrix of the EKF the standard deviation of each robot-wheel's angular speed is estimated as being proportional to the wheel's angular speed. A correction step is performed by minimizing the difference between the matched line segments from the local and global maps. If the overlapping rate between the most similar local and global line segments is below the threshold, the line segments are paired. The line parameters' covariances, which arise from the LRF's distance-measurement error, comprise the output noise covariance matrix of the EKF. The covariances are estimated with the method of classic least squares (LSQ). The performance of this method is tested within the localization experiment in an indoor structured environment. The good localization results prove the applicability of the method resulting from the classic LSQ for the purpose of an EKF-based localization of a mobile robot. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for tracking-error model-based Parallel Distributed Compensation (PDC) control for non-holonomic vehicles where the outputs (measurements) of the system are delayed and the delay is constant. Briefly, this technique consists of rewriting the kinematic error model of the mobile robot tracking problem into a TS fuzzy representation and finding a stabilizing controller by solving LMI conditions for the tracking-error model. The state variables are estimated by nonlinear predictor observer where the outputs are delayed by a constant delay. To illustrate the efficiency of the proposed approach a comparison between the TS fuzzy observer and the nonlinear predictor observer is shown. For this study the reference trajectory is built by taking into account the acceleration limits of the mobile robot. All experiments are implemented on simulation and the real-time platform. [ABSTRACT FROM AUTHOR]

In this paper a case study of a new, cooperative, collision-avoidance method for multiple, nonholonomic robots based on Bernstein-Bézier curves is given. In the presented examples the velocities and accelerations of the mobile robots are constrained and the start and the goal velocity are defined for each robot. This means that the proposed method can be used as a subroutine in a huge path-planning problem in real time, in a way to split the whole path into smaller partial paths. The reference path of each robot, from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the path lengths subjected to the distances between the robots, which should be bigger than the minimum distance defined as the safety distance, and subjected to the velocities and accelerations which should be lower than the maximum allowed for each robot. When the reference paths are defined the model-predictive trajectory tracking is used to define the control. The prediction model derived from the linearized tracking-error dynamics is used to predict future system behavior. The control law is derived from a quadratic cost function consisting of the system tracking error and the control effort. The proposed method was tested with a simulation and with a real-time experiment in which four robots were used. [ABSTRACT FROM AUTHOR]