Building-management systems (BMSs) are becoming increasingly important as they are an efficient means to having buildings that consume less energy as well as for improving the indoor working and living environments. On the other hand, implementing automated control and monitoring systems in buildings is still relatively new, and one of the obstacles for their wider implementation is the ease of setting up the appropriate parameters for the controllers. During our work on an experimental controller for an indoor environment that is installed in an occupied office in the building of the Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia, it has become evident that a computer simulator of the system would be a welcome aid for the optimization of its functioning. In this paper we present a simulator application developed in a combined Matlab/Simulink and Dymola/Modelica environment. The simulator mirrors the functioning of the control system and the dynamics of the indoor environment, where the thermal model of the simulator was developed in the Dymola/Modelica environment, while the illuminance model was developed and parameterized as a black-box model on the basis of measurements in the Matlab environment. The simulator can emulate the response of conventional ON/OFF controllers as well as fuzzy controllers. The paper presents the design of the simulator with all of the key elements described. The underlying models for the thermal and illuminance control are also separately described. Finally, the performance of the simulator is presented for a selected day.
COBISS.SI-ID: 10062676
In this paper a method for estimating the relative orbit is proposed. The method requires a minimum number of simple sensors. The design of observers for formation-flying control, which is formulated as a control problem of tracking the target satellite, is treated. If the equations of the relative motion of the target and the chaser satellites are rewritten in the Local Vertical/Local Horizontal (LVLH) coordinate system, a nonlinear control system of the sixth order is obtained. It is very well known that such a control problem can be solved by a (linear or nonlinear) state controller. The formation-flying models are reviewed and analysed with respect to their observability according to the measured quantities. Based on the results of the observability analysis two state observers enabling an estimation of all the states are proposed in the paper: a simple observer of the linearised system and a nonlinear observer. Since cheap, small satellites are targeted, the application of cheap sensors is studied. In addition, the possibility of measuring the three relative position coordinates of the chaser satellite with a camera and a compass is given with some simulation results demonstrating the suppression of the measurement noise, which is significant when using cheap, COTS sensors and cameras.
COBISS.SI-ID: 9168980
In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is designed for use on a non-linear single-input single-output system. The control algorithm is developed based on the Takagi-Sugeno fuzzy model, and it consists of two loops: a feedforward loop and feedback loop. The feedforward part of the controller should drive the system output to the vicinity of the reference signal. It is developed from the inversion of the T-S fuzzy model. To achieve accurate error-free reference tracking a feedback part of the controller is added. A time-varying error-model predictive controller is used in the feedback loop. The error-model is obtained from the T-S fuzzy model. The T-S fuzzy model of the system, required in the controller, is obtained with evolving fuzzy modelling, which is based on recursive Gustafson-Kessel clustering algorithm and recursive fuzzy least squares. It employs evolving mechanisms for adding, removing, merging and splitting the clusters. The presented control approach was experimentally validated on a non-linear second-order SISO system helio-crane in simulation and real environment. Several criteria functions were defined to evaluate the reference-tracking and disturbance rejection performance of the control algorithm. The presented control approach was compared to another fuzzy control algorithm. The experimental results confirm the applicability of the approach.
COBISS.SI-ID: 10289236
In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders andfrom laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.
COBISS.SI-ID: 9708116
Original contributions of complex systems control problems are presented in this book.
COBISS.SI-ID: 9548628