1,720,984 research outputs found
Complete and Consistent Payload Identification During Human-Robot Collaboration: A Safety-Oriented Procedure
No full textThe paper proposes a procedure to provide a complete and physically-consistent estimation of mass, center of mass and inertia tensor of the payload attached to the end-effector of an industrial manipulator equipped with a force/torque sensor. The procedure involves the generation of an artificial potential field that allows the proper excitation of the payload inertial parameters while avoiding static and dynamic obstacles, thus ensuring a safe and collaborative scenario. The adopted identification algorithm consists in the solution of a constrained non-linear optimization problem that guarantees the physical consistency of the inertial parameters. The proposed approach has been validated by simulating a typical collaborative workcell where a Franka-Emika Panda robot performs the procedure while avoiding dynamic obstacles
Data–Driven Design of an Active Wake Steering Control for a Wind Farm Benchmark
No full textWake steering yaws upstream wind turbines to deflect their wakes from downstream turbines, thus increasing the generated power. However, most wake steering methods rely on lookup tables obtained offline, which map a set of conditions, such as wind speed and direction, to yaw angles for each turbine in a farm. These tables assume all turbines are operational and can be significantly non–optimal when one or more turbines do not provide the rated power, because of low wind speed, faults, routine maintenance, or emergency maintenance. This work presents an intelligent wake steering method that adapts to turbine actual working conditions when determining yaw angles. Using a hybrid model–and a learning–based method, i.e. an active control, a neural network is trained online to determine yaw angles from operating conditions including turbine status. Unlike purely model–based approaches which use lookup tables provided by the wind turbine manufacturer or generated offline, the proposed control solution does not need to solve e.g. optimisation problems for each combination of the turbine non-optimal working conditions in a farm; the integration of learning strategy in the control design allows to obtain an active control scheme
Hardware-In-The-Loop Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator
No full textTo enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real- time solutions have to implement the proposed fault tolerant control strategy. Finally, a high- fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations
Actuator Fault Reconstruction via Dynamic Neural Networks for an Autonomous Underwater Vehicle Model
Full text linkThis paper proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous underwater vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time-varying sea currents. The proposed fault detection and isolation strategy uses a data-driven approach relying on multi-layer perceptron neural networks that include auto-regressive exogenous prototypes that provide the fault reconstruction. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and outputmeasurements acquired from the simulator. In this work, the residuals are designed to represent the reconstruction of the fault signals themselves. Moreover, the neural network bank is also able to perform the isolation task, in case of simultaneous and concurrent faults affecting the actuators. The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high-fidelity simulations of a realistic autonomous underwater vehicle, in the presence of faults and marine current
Hardware-In-The-Loop Assessment of Fuzzy and Neural Network Fault Diagnosis Schemes for a Wind Turbine Model
No full textThe fault diagnosis of wind turbines includes extremely challenging aspects that motivate the research issues considered in this paper. In particular, this work studies fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis techniques that exploits the estimation of the fault by means of data-driven approaches. These fuzzy and neural network structures are integrated with auto-regressive with exogenous input regressors, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of fault diagnosis schemes are validated by using a real-time simulator of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. This realistic simulator relies on a hardware-in-the-loop tool that is finally implemented for verifying and validating the performance of the developed fault diagnosis strategies in an actual environment
Design and Validation of a Fault Tolerant Fuzzy Control for a Wind Park High–Fidelity Simulator
No full textTo enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data–driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input–output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data–driven approach, thus representing the key point if real–time solutions have to implement the proposed fault tolerant control strategy. Finally, a high–fidelity simulator including hardware–in–the-loop modules is exploited to validate the reliability and robustness characteristics of the developed methodologies also for on–line implementations
Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark
No full textUpstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained online to calculate yaw angles from operating data, including turbine status, using a hybrid model and learning-based active control method. The proposed control solution does not need to solve optimization problems for each combination of the turbines' non-optimal working conditions in a farm, as opposed to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. Instead, the integration of learning strategy into the control design enables the creation of an active control scheme. The suggested methodology differs from solely learning-based approaches in that it doesn't call for a significant number of training samples, such as in model-free reinforcement learning. In actuality, by taking use of the model during backpropagation, the suggested approach learns more from each sample. Using a well-known and practical wind farm benchmark, results are reported for both standard (nominal) wake steering under operational conditions with all turbines and for faulty conditions
Planning Collision-Free Robot Motions in a Human–Robot Shared Workspace via Mixed Reality and Sensor-Fusion Skeleton Tracking
No full textThe paper describes a method for planning collision-free motions of an industrial manipulator that shares the workspace with human operators during a human–robot collaborative application with strict safety requirements. The proposed workflow exploits the advantages of mixed reality to insert real entities into a virtual scene, wherein the robot control command is computed and validated by simulating robot motions without risks for the human. The proposed motion planner relies on a sensor-fusion algorithm that improves the 3D perception of the humans inside the robot workspace. Such an algorithm merges the estimations of the pose of the human bones reconstructed by means of a pointcloud-based skeleton tracking algorithm with the orientation data acquired from wearable inertial measurement units (IMUs) supposed to be fixed to the human bones. The algorithm provides a final reconstruction of the position and of the orientation of the human bones that can be used to include the human in the virtual simulation of the robotic workcell. A dynamic motion-planning algorithm can be processed within such a mixed-reality environment, allowing the computation of a collision-free joint velocity command for the real robot
Data-Driven Fault Detection and Isolation of the Actuators of an Autonomous Underwater Vehicle
No full textThis work proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous under-water vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time-varying sea currents. The proposed fault detection and isolation strategy uses a data-driven approach relying on multi-layer perceptron neural networks that include auto-regressive exogenous prototypes. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and output measurements acquired from the simulator. The neural network bank is able to provide the detection of the faults affecting the actuators jointly with their isolation in case of simultaneous and concurrent faults The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high-fidelity simulations, in presence of faults and marine current
Validation of data-driven fault diagnosis strategies for a wind turbine test rig
No full textThe fault diagnosis of safety critical systems, such as a wind turbine plant, represents a difficult issue, especially for offshore installations, which motivates the investigations addressed in this work. In fact, these systems should be able to maintain specified operable and committable conditions, and at the same time, should avoid expensive unplanned maintenance works. Therefore, this paper considers this problem and develops a data–driven fault diagnosis approach that is verified on a wind turbine high–fidelity test-rig. In particular, the proposed design derives nonlinear filters that provide the estimation of the fault by using the input–output data acquired from the monitored process. Moreover, the proposed approach represents an effective method for managing data affected by measurement errors, disturbance and model–reality mismatch. In more detail, the developed strategies exploit fuzzy systems and neural networks, which are able to derive the nonlinear dynamic functions between input–output measurements and faults. Moreover, these dynamic nonlinear structures represented by fuzzy prototypes include autoregressive with exogenous input structures, with the ability to approximate any nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of the developed fault estimators thus exploited for monitoring and fault diagnosis purpose are verified using a wind turbine test–rig, which allows also to analyse their robustness and reliability features. In fact, this test–bed relies on a hardware–in–the–loop technique that is able to take into account uncertainty and disturbance, thus emulating a very realistic environment
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