Application of predictive control for manipulator mounted on a satellite

Specific conditions of on-orbit environment are taken into account in the design of all devices intended to be used in space. Despite this fact malfunctions of satellites occur and sometimes lead to shortening of the satellite operational lifetime. It is considered to use unmanned servicing satellite, that could perform repairs of other satellites. Such satellites equipped with a manipulator, could be used to capture and remove from orbit large space debris. The critical part of planned missions is the capture manoeuvre. In this paper a concept of the control system for the manipulator mounted on the satellite is presented. This control system is composed of the trajectory planning module and model predictive controller (the latter is responsible for ensuring precise realization of the planned trajectory). Numerical simulations performed for the simplified planar case with a 2 DoF manipulator show that the results obtained with the predictive control are better than the results obtained with adaptive control method

Self-organizing robot formations using velocity potential fields commands for material transfer

Mobile robot formations differ in accordance with the mission, environment, and robot abilities. In the case of decentralized control, the ability to achieve the shapes of these formations needs to be built in the controllers of each autonomous robot. In this paper, self-organizing formations control for material transfer is investigated, as an alternative to automatic guided vehicles. Leader–follower approach is applied for controllers design to drive the robots toward the goal. The results confirm the ability of velocity potential approach for motion control of both self-organizing formations

Sensor fusion

Sensor fusion is a method of integrating signals from multiple sources. It allows extracting information from several different sources to integrate them into single signal or information. In many cases sources of information are sensors or other devices that allow for perception or measurement of changing environment. Information received from multiple-sensors is processed using "sensor fusion" or "data fusion" algorithms. These algorithms can be classified into three different groups. First, fusion based on probabilistic models, second, fusion based on least-squares techniques and third, intelligent fusion. The probabilistic model methods are Bayesian reasoning, evidence theory, robust statistics, recursive operators. The least-squares techniques are Kalman filtering, optimal theory, regularization and uncertainty ellipsoids. The intelligent fusion methods are fuzzy logic, neural networks and genetic algorithms. This paper will present three different methods of intelligent information fusion for different engineering applications. Chapter 2 is based on Sasiadek and Wang (2001) paper and presents an application of adaptive Kalman filtering to the problem of information fusion for guidance, navigation, and control. Chapter 3 is based on Sasiadek and Hartana (2000) and Chapter 4 on Sasiadek and Khe (2001) paper

Adaptive Fuzzy Logic System for sensor fusion in dead-reckoning mobile robot navigation

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Repetitive learning with fuzzy logic adaptive control of a flexible robot manipulator

Operational problems with robot manipulators in space relate to several factors, one most importantly being structural flexibility and subsequently significant difficulties with the control systems, especially, position control. A control strategy is devised for positioning the endpoint of a two-link robot manipulator modeled with assumed modes flexible dynamics repetitively tracking a square trajectory. The dominant assumed modes of vibration are determined for Euler-Bemoulli cantilever beam boundary conditions then, coupled with the nonlinear dynamics for rigid links to form an Euler-Lagrange inverse flexible dynamics robot model. A Jacobian transpose control law actuates the robot links. While repetitive tracking alone achieves no improvement in control precision, adapting the control law by a fuzzy logic system achieves consistent tracking precision

Decentralized simple adaptive control of nonlinear systems

Recently, the passivity results for linear time-invariant systems were successfully extended to nonlinear and nonstationary systems, thus guaranteeing stability of adaptive control of nonlinear square systems. Based on this theoretical development, this paper presents the development of a new class of direct adaptive controllers, which employ a new decentralized adaptation law mechanism that is developed from the simple adaptive control technique. The resulting direct adaptive control methodology is referred to as decentralized simple adaptive control. A simplification of this new control algorithm, referred to as decentralized modified simple adaptive control, is also presented. In addition, it is shown that both control methodologies can be modified to avoid divergence in practical situations, where the trajectory tracking errors cannot reach zero. Using Lyapunov direct method and Lasalle's invariance principle for nonautonomous systems, the formal proof of stability is established. As well, a numerical simulation study for a trajectory tracking problem by a rigid-joint manipulator is presented to illustrate the new adaptive control approaches. Copyrigh