This work is part of “Operational Network of Individual Observation Nodes” (ONION). ONION is an H2020 project devoted to identify solutions and to satisfy emerging needs of the European Earth Observation (EO) market. In this regard, this work presents a review and analysis of the potential payloads and next generation of sensors in order to cover the measurements gaps of the top ten use cases of the European EO infrastructure. For each use case the potential instruments to support stakeholder requirements are identified, pointing out the aspects in which they represent an innovation with respect to existing technological solutions. This identification of instruments is based on existing, operational, under development, and demonstration missions. The capability of the instrument technologies is evaluated according to the trends in the design for small EO satellites (payload mass <; 500 kg). This work concludes with a set the potential instruments to be possibly developed as part of the future European space infrastructure, proposing an integrated set of instruments and measurements to optimize the performances, simplifying the implementation, and reducing the costs.

In this work, a Sensorless hybrid control system is proposed for PMSMs requiring only knowledge of flux linkage. The control scheme consists of two adaptive fuzzy logic controllers for speed and direct current control, respectively. On the other hand, an Artificial Neural Network (ANN) based observer is used to estimate rotor speed and the rotor position is obtained by using integration to reduce the effect of the system's noise. Unlike other sensorless control strategies, this control strategy does not require any prior machine parameter identification. The results for different situations clearly show the good performance of the proposed control strategy.

Optimal controller and anti-windup tuning for motion systems with flexible coupling and cascaded loops control structure is a practical and difficult problem, in particular when time scale separation is insufficient. An Ant Colony Optimization (ACO) based methodology for global off-line tuning of Proportional-Integral-Derivative (PID) controllers with anti-windup can be used at the expense of excessive computation time. A new hybrid algorithm based on ACO and Nelder-Mead (NM) improves computation time to reach accurate optimal solutions. This hybrid algorithm handles large sets of parameters to tune and the designer can modify or create new performance indices to fully grasp the desired behavior with multi-objective criteria.

In this paper, a sensorless artificial neural network (ANN) speed control strategy of permanent magnet synchronous machines (PMSMs) is introduced as an alternative to conventional control techniques. The control strategy achieves accurate tracking by making use of ANN's learning capabilities to approximate the machine's nonlinear dynamics. On the other hand, an ANN-based observer is used to estimate rotor speed and the rotor position is obtained by direct integration to reduce the effect of the system's noise. Unlike other sensorless control strategies, no a priori of œine training, weights initialization, voltage transducer or mechanical parameters knowledge is required. Furthermore, the stability of the overall closed-loop system is proved by Lyapunov stability theory. The controller is compared to the well-known vector control technique. Results for different situations highlight the higher performance of the proposed control approach in transient, steady-state, and standstill conditions.

Many types of materials are manufactured or processed in the form of a sheet or a web (textile, paper, metal, etc.), which then couples the processing rolls and the associated motor drives. In Winding and web transport systems, the main concern is to control independently speed and tension in spite of disturbances such as radius variations and changes of set point, and to the coupling introduced by the elastic web. So the increasing requirement on the control performance led to investigation for sophisticated control strategies. To improve the overall performance of the winding system, we propose to apply decentralized control based an Integrator Backstepping strategy for controlling web winding systems. The advantage of this type of control is to impose stability properties by building recursively a Lyapunov function for the overall cascade system. Our winding system is simulated in Matlab®/Simulink™ environment, the results obtained illustrate the efficiency of the proposed control, with no overshoot, and the rising time is improved with good disturbance rejection comparing with the classical control law (PI controller).

A hybrid control system for PMSM that requires rough knowledge of the machine's quadrature inductance only is proposed. The scheme consists of an adaptive fuzzy speed controller and a sliding mode current controller. Lyapunov stability conditions are used to design the controllers that result into low sensitivity to torque disturbances and parameter variations.

In this paper, an adaptive vector control scheme is introduced for permanent magnet synchronous machines (PMSMs) as an alternative to classical control techniques. The adaptive control strategy capitalizes on the machine's inverse dynamics to achieve accurate tracking by using an observer to approximate disturbance in the form of friction and load torque. The controller's output is then fed to a Space Vector Pulse Width Modulation (SVPWM) algorithm to produce duty cycles for the inverter. The observer also provides a rotor speed estimate, which is used as feedback. The control scheme is validated through a set of simulations on an experimentally validated PMSM model. Results for different situations highlight its high speed estimation accuracy and high performance in compensating for friction and load disturbances of various magnitudes.

An adaptive control strategy has been developed for flexible-joint robotic manipulators in the presence of friction nonlinearities and external disturbances. As the exact inverse model is unrealizable for such systems, only an approximation can be found. The control strategy consists of a rigid linear in parameter model based feedforward controller that approximates the flexible-joint inverse model and a neural network feedback controller that compensates for parametric and modeling uncertainties such as, friction, flexibility, and disturbance. A reference model is used as a trade off strategy to alleviate joint elasticity effects. Unlike other control strategies, no a priori offline training or weights initialization is required. Results with different situations highlight the performance of the adaptive controller in compensating for structured and unstructured dynamical uncertainties, in particular nonlinear Coulomb friction terms and external disturbance. Internal stability, a potential problem with such a system, is also verified. Furthermore, the adaptive control structure stability is guaranteed by Lyapunov stability theory.

This work presents a neural network based modeling strategy to precisely identify the thrusts of cold-gas thrusters deployed in a nano-satellite experimental test-bed developed at the Canadian Space Agency (CSA). Eight thrusters are used to control the planar motion of an emulated free-floating spacecraft supported by air-bearing. Calibration experiments conducted on these thrusters revealed that the generated thrusts are highly nonlinear with respect to their inputs, the digital openings and the air pressure. Motivated by the learning and approximation capabilities of artificial neural networks (ANNs), an ANN is used to model the nonlinear thruster behavior using experimental data. The performance of the proposed strategy is satisfactory and clearly demonstrated by the resulting high precision model.

Sensorless vector control is an instantaneous electromagnetic torque control technique applied to variable speed (AC) motor drives where the costly speed sensor is no longer used. It is characterized by its fast dynamic response and low cost. Sensorless variable speed drives incorporating induction machines immerged as a result of the progress in the field of power electronics and digital signal processing (DSP) technology. DSP substantially helped the implementation of complex control algorithms and the generation of patterns to control voltage source inverters. Despite the fact that the DSP technology is widely used in the industry and manufacturing data sheets are available, there are few comprehensive documents in the literature detailing the principles of development and implementation of the new generation of DSP. This paper presents the development of a DSP-based sensorless vector control of induction machine. The algorithm development methodology and the experimental results are presented for the TMS320F2812 processor.

Sensorless vector control is applied to variable speed (AC) motor drives where speed estimators and observers are used instead of the costly speed or position sensors. Eliminating speed and position sensors reduces hardware complexity and cost, increases the mechanical robustness and reliability of the drive, and leads to a better noise immunity. However, some of these speed estimation techniques could be used only in low-cost drive applications not requiring high dynamic performance. The paper presents analytical study and simulation results of induction machine speed sensorless vector control where various speed estimation techniques are discussed.

This paper presents a full-differential analog design of an indirect inverse control law based on dynamic back propagation neural networks developed. The on-line adaptation algorithms of the synaptic weights are modeled by means of continuous-time integration circuits. The simulation results obtained at a post-layout simulation level show a very good computing precision as well as interesting power consumption and integration area. The speed of the circuit is largely sufficient to meet real-time requirements in numerous applications of the control fields

Robotics manipulators may exhibit flexibility that adversely affects the dynamic and even static performance of controllers designed for rigid robots. Full state measurement is costly, so that part of the state must often be reconstructed. We present the design of two observers: 1) a variable structure observer requiring the measurement of link positions to estimate the full state of a flexible joint manipulator; and 2) a reduced adaptive observer that requires the measurement of link and motor positions. Stability and robustness properties are established and we propose a methodology to design the variable structure observer by solving simultaneously a Lyapunov equation and a matching condition. Simulation results are provided to evaluate their performance.

An automatic joint guidance system was developed as part of a project to develop an expert welding robot. The apparatus consists of a servo-robot laser scanner mounted on a conventional CLOOS model ROMAT 75/76 industrial welding robot. The computer vision system is able to recognize and track the joint to be welded without external information by using range data obtained from scanning the workpiece. An approach and some experiments related to achieving this ultimate goal are discussed. Three different joint types are identified. Tracking of the joint geometry is done by building, online, a three-dimensional model of the joint during the welding process. This approach is attractive because of its complete autonomy for joint recognition and its ability to trace the geometry while at the same time avoiding an elaborate teaching pass prior to the process. The algorithms used have been tested in a simulation using real laser data. The results indicate that it is possible to recognize automatically the joint type and then to track the joint during the process of welding.>

Adaptive control and sensory processing techniques in robotic arc welding are discussed. The gas metal arc welding and gas tungsten arc welding processes are considered, along with a literature review of aspects of welding automation. Topics covered include process modeling, detection and measurement of process features, real-time control, and implementation considerations. An approach for an adaptive welding system is presented. The proposed architecture fits within the scope of an ambitious project to develop an expert welding robot. Different levels of automation are discussed, from the decision level to the closed-loop control of process variables and torch trajectory.>