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Possible approaches to building the information and mathematical models to evaluate of the effectiveness and quality of the University are discussed in this paper. We characterize cycle of university management, determine the factors affecting the performance activity of universities, identify indicators of assessment of effectiveness and quality, formulate the problem of university management through...
Post-processing indoor navigation is interesting, for example to develop crowdsourcing analysis. The post-processing framework allows to provide a better estimation than in a real-time framework. The main contribution of this paper is to present a piecewise parametrization using Inertial Measurement Unit (IMU) and Received Signal Strength (RSS) measurements only which lead to an optimization problem...
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting received signal strength (RSS) measurements for Wifi access points (APs) are proposed...
Considering the connection between black-box optimization problem and reinforcement learning (RL) problem, we can solve a RL problem by black-box optimization algorithm. Especially, extremum seeking (ES) is a notable black-box optimization algorithm, but there exist two studies which employs ES to solve a specific RL problem. In this study, we formulate such a general RL problem as which has a stochastic...
This work provides a comparative review of three different numerical methods generally used to discretize continuous-time non-linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the characteristics of each method is given and the performance of each method is evaluated through the simulation of...
Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes...
In practice, high quality control for mechatronic systems is often achieved by augmenting classical control architectures like PID controllers with numerous tailored nonlinear characteristic parameter curves and cascades. This complexity can be significantly reduced by utilizing advanced model predictive controllers (MPC). Furthermore, desired objectives like minimum control error and effort can be...
In context of human-machine-interaction trajectories need to be rerouted or re-planned with reduced dynamical constraints in order to avoid collisions. This requires online feasible algorithm for trajectory planning in space. In this paper a new method for planning time-optimal trajectories subject to dynamic and geometrical constraints is proposed. Distinctively to other work, both spatial and temporal...
A path optimization strategy for generating energy-aware soaring trajectories for small Unmanned Aerial Systems (sUAS) is presented. Soaring paths are generated using a point-mass dynamics model and deterministic local wind field. The dynamics model, periodic constraints, and an energy-aware cost function are combined into a nonlinear program (NLP) framework to generate energy optimal paths. A dynamics...
This paper presents a dynamic collision avoidance controller of a connected vehicle group using Model Predictive Control (MPC). The vehicles should follow a predefined reference trajectory while simultaneously avoiding collisions on this trajectory. MPC pursues the tracking of the reference trajectory in the objective function of the optimization problem. In order to avoid collisions, MPC defines...
This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data....
We perform a comparative analysis of energy-based performance criteria for the dynamics-based optimization of robot trajectories. The performance criteria considered include minimum torque, electrical power loss, approximation to mechanical work, and energy loss due to friction. Our dynamics model takes into account rotor inertias and gearing, and also considers robots subject to a range of motion...
The paper presents the outline of the didactical project of a complex computer controlled system realized by the first degree students of the Automatics and Robotics on the Faculty of Electrical and Control Engineering (FoEaCE) in Gdansk University of Technology (GUT). The synthesis, implementation and analysis of a multilayer hierarchical control system for drinking water supply system (DWSS) are...
In this paper, we present a mid-level collision avoidance algorithm for autonomous surface vehicles (ASVs) based on model predictive control (MPC) using nonlinear programming. The algorithm enables avoidance of both static and moving obstacles, and following of a desired nominal trajectory if there is no danger of collision. We compare two alternative objective functions, where one is a quadratic...
This paper presents a real-time altitude optimization technique for airborne wind energy (AWE) systems that fuses a coarse, global optimization with a fine, local optimization. The ultimate goal is to maximize net energy consumption by operating at an altitude where the wind speed is closest to the turbine's rated wind speed. Without the use of auxiliary wind profiling equipment, this results in a...
This paper presents a novel approach for timeoptimal model predictive control. In contrast to a global uniform time scaling, the underlying optimal control problem rests upon a dynamic, local temporal discretization of the shooting grid. The approach seeks for a grid partition with minimum overall transition time. Furthermore, a multi-stage optimization iteratively adapts the number of grid points...
Model-based predictive control is an effective method for control the large scale systems. Method is based on on-lin solution of control task over the control horizon using current and past measurements as well as the system model. Because model and measurement uncertainty, predicted and plant outputs might be different and plant output may exceed plant output constraints. Generated control is not...
This paper addresses the decision problem for terrain-following flights (TFFs) of aircrafts in unknown environments, where global trajectory planning methods cannot be applied directly. In such cases, the uncertainties and complexities of the terrain are needed to be dealt with especially. In order to realize the automatic generation of a terrain-following (TF) controller, a self-learning TF method...
A method based on variable horizon predictive control is proposed in the paper for nominal trajectory guidance. Firstly, a feasible reference trajectory is generated by the Gauss pseudospectral method. Then, curvature is employed to represent the smoothness of obtained trajectory and predictive horizon is selected according to the curvature. Different predictive control optimal problems are established...
This paper presents a kinematic model based path planning algorithm for autonomous vehicle. Polynomial parameterization is used to generate the trajectory. All constraints including kinematic constraints and obstacles are represented by polynomial parameters. Using this parameterization method the velocity can be planned simultaneously. Road information is taken into account in the object function...
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