Asier Zubizarreta

A procedure to evaluate Extended Computed Torque Control configurations in the Stewart–Gough platform

Abstract Parallel robots have become the best solution when high speed and/or accuracy are needed in industrial robotic operations. However, in order to meet the requirements of these tasks, advanced model based controllers such as the Extended CTC scheme are required. This CTC-based scheme requires the introduction of extra sensors in the passive joints of the parallel robot. This redundant information allows to increase the robustness and performance of the control law, leading to better trajectory tracking. However, in order to achieve the best performance, a proper extra sensor distribution is required. In this paper, a sensitivity analysis based approach is applied to the well known Gough Platform in order to evaluate different extra sensor distributions. The obtained results are compared with those obtained by a statistically significant set of simulations, demonstrating the effectiveness of the methodology.

Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses

This paper presents the use of artificial neural networks (ANN) to diagnose degraded behaviours in wire electrical discharge machining (WEDM). The detection in advance of the degradation of the cutting process is crucial since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy. Concerning this, previous investigations have identified different types of degraded behaviours in two commonly used workpiece thicknesses (50 and 100mm). This goal was achieved by monitoring different functions of characteristic discharge variables. However, the thresholds achieved by these functions depended on the thickness of the workpiece. Consequently, the main objective of this work is to detect the degradation of the process when machining workpiece of different thicknesses using one unique empirical model. Since artificial neural network techniques are appropriate for stochastic and non-linear nature processes, its use is investigated here to cope with workpieces of different thicknesses. The results of this work show a satisfactory performance of the presented approach. The satisfactory performance is shown by two ratios: the validation ratio, which ranges between 85% and 100%, and the test ratio, which results between 75% and 100%.

Control of parallel robots using passive sensor data

A novel control architecture for parallel robots is introduced to fully exploit the advantages of these robots on high-speed and precision operation. A closed form of the dynamic model of parallel robots is difficult to obtain, due to the complex kinematic relations of these kind of mechanism. However, with the use of the extra data provided by sensors placed in strategic passive joints, kinematic and dynamic modelling can be simplified. The dynamic model can be used to implement advanced control techniques to improve the efficiency of parallel robots. In this paper, monoarticular and multiarticular control techniques are implemented on a 5R parallel robot, showing that the use of extra sensor data leads to a better and accurate control.

A redundant dynamic model of parallel robots for model-based control

The use of extra sensors in parallel robots can provide an increase in control performance, making it possible to fully exploit the potential of these mechanisms. In this paper, a comprehensive redundant dynamic modelling procedure for the six-degree-of-freedom Gough platform is presented. The proposed methodology makes it possible to define the model in terms of all sensorized joint variables in order to implement redundant information-based control, and an example, the Extended Computed Torque Control Extended CTC approach, is developed. This, applied to parallel robots, ensures better dynamic performance than the traditional CTC approach. In order to validate dynamic modelling, a two-step procedure is used in this paper. First, the redundant dynamic model is validated by comparing its dynamic performance with the previous research in the field. Second, an exhaustive study is carried out that demonstrates the advantages of the redundant dynamic model when used in the Extended CTC approach.

Real time direct kinematic problem computation of the 3PRS robot using neural networks

Abstract The reliable calculation of the Direct Kinematic Problem (DKP) is one of the main challenges for the implementation of Real-Time (RT) controllers in Parallel Robots. The DKP estimates the pose of the end effector of the robot in terms of the sensors placed on the actuators. However, this calculation requires the use of time-consuming numerical iterative procedures. Artificial Neural Networks have been proposed to implement the complex DKP equation mapping due to their universal approximator property. However, the proposals in this area do not consider the Real Time implementation of the ANN based solution, and no approximation error vs computational time analysis is carried out. In this work, a methodology that uses Artificial Neural Networks (ANNs) to approximate the DKP is proposed. Based on the 3PRS parallel robot, a comprehensive study is carried out in which several network configurations are proposed to approximate the DKP. Moreover, to demonstrate the effectiveness of the approach, the proposed networks are evaluated considering not only their approximation capabilities, but also their Real Time performance in comparison with the traditional iterative procedures used in robotics.

Downsizing training data with weighted FCM for predicting the evolution of specific grinding energy with RNNs

Abstract Grinding plays a prominent role in modern manufacturing due to its capacity for producing parts of high accuracy and precision. Among the various grinding variables, the specific grinding energy (e c ) is key because it measures the energy required to remove the unit volume of part material and therefore it gives information about the performance of the grinding process. In addition, a measure of the specific grinding energy is also useful for estimating the power requirements of the grinding machine. Thus, a Recurrent Neural Network (RNN) is used for predicting e c in a more realistic manner that involves the development of the e c over time. Moreover, since performing grinding experiments is a highly time and resource consuming task, it would be very useful to downsize the required dataset to train the RNN since it could substantially reduce the time and costs involved in carrying out the experiments to generate the dataset, as well as in training the RNNs. Therefore, in this work a methodology combining Fuzzy C-Means (FCM) and RNNs for downsizing the dataset and predicting specific grinding energy is proposed. Unlike other approaches for reducing the dataset using FCM, in the current work the inputs are weighted. To achieve this, the knowledge is extracted from the weights of satisfactorily trained RNN obtained from previous work. The results show that under reduced training datasets (weighted and non-weighted FCM inputs) and non-reduced datasets (all available experiments), superior results were yielded with the RNNs obtained with the weighted approach. In fact, in some cases, for the reduced training dataset (weighted) the error is halved. Furthermore, the results show that it is more advantageous to use a reduced training dataset obtained after FCM, since this reduces the costs associated with experimental time, as well as the training time required for RNNs.

Experimental validation of the extended computed torque control approach in the 5r parallel robot prototype

Parallel robots have become an interesting alternative to serial robots due to their capability to perform certain tasks at high speed and precision. However, in order to fully exploit the theoretical capabilities of these mechanism, model-based, advanced control approaches are required. In this paper, the Extended CTC control approach is introduced. This scheme is based on the introduction of the data of the passive joint sensors on a CTC-based control law. Experimental validation on a 5R parallel manipulator prototype is provided in order to demonstrate the effectiveness of the approach.

Extended CTC control for parallel robots

Parallel robots have recently arisen as an interesting robotic architecture capable of performing task at high speed and precision. In order to exploit all the potential of these mechanism, model based control approaches are required. However, due to their complex structure, kinematic and dynamic modelling is a complex task, which usually leads to models with parameter uncertainties. In order to reduce the effect of parameter uncertainties, in this paper a redundant dynamic model based Extended CTC approach is proposed, and its stability and sensitivity analyzed for the 3RRR parallel robot. Results show that this approach provides more robustness than classical CTC approach to model parameter uncertainties.

Dynamic modeling of planar parallel robots considering passive joint sensor data

Model-based advanced control approaches are needed to achieve high speed and acceleration and precision in robotic operations. These control schemes need a proper dynamic model. Many approaches have been proposed by different authors in order to obtain the dynamic model of these structures. However, most of them do not consider the possibility to introduce redundant sensor data. In this paper, a methodology for obtaining a compact dynamic model considering passive joint sensor data is proposed. The dynamic model is defined in compact and structured form, which makes it appropriate to be used in advanced control techniques.

Virtual Sensors for Advanced Controllers in Rehabilitation Robotics

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.