Gait analysis can provide relevant information about the physical and neurological conditions of individuals. For this reason, several studies have recently been carried out in an attempt to monitor people's gait and automatically detect gait anomalies. Among the various monitoring systems available for gait analysis, wearable sensors are considered the gold standard due to their wide capture range and low cost. However, in the case of people that require assistive devices for walking, some studies have proposed the use of sensorized devices in order to minimize invasiveness. Nevertheless, there is still a lack of comparative works that evaluate the performance of sensorized assistive devices for walking with widely used wearable sensors. Hence, this paper presents a comparison between the performance of accelerometer-based wearable sensors and a sensorized tip developed by the authors to detect gait anomalies. The comparative study has been carried out in a controlled environment with five healthy subjects, in which three different physical states have been simulated. A machine-learning based anomaly detection approach has been implemented based on the data captured by a set of wearable sensors and the sensorized tip, and the overall performance of both monitoring systems has been evaluated. Results show that even if both devices can provide an average accuracy of more than 80% in gait anomaly detection, the sensorized tip provides better performance.

Abstract Lower limb motor impairment affects greatly the autonomy and quality of life of those people suffering from it. Recent studies have shown that an appropriate rehabilitation can significantly improve their condition, but, for this purpose, it is essential to know the patient’s functional state and to be able to detect any changes that occur in it as soon as possible. Traditionally, standardized clinical scales have been used to make that assessment, however, as the number of patients to be assessed is high, assessment frequency is usually low. In response to this problem, the aim of the present work is to design a new personalized methodology for developing a Machine Learning-based gait anomaly detector that is able to detect significant changes in the functional state of patients based on data provided by a sensorized tip; a system that will serve as support for the therapist who is treating the monitored patient’s case. Taking into account the variability that exists among patients, the proposed design focuses on an individualized approach, so that the system characterizes the state change of each patient case only on his/her own data. Once developed, the proposed methodology has been validated in ten healthy people of different complexions, achieving an average accuracy of 87.5%. Finally, five case studies have been analyzed, in which data from five multiple sclerosis patients have been captured and studied, obtaining an average accuracy of 82.5%.

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Low switching frequency modulation techniques such as Selective Harmonic Elimination - Pulse Width Modulation (SHE-PWM) or Optimal Pulse Pattern (OPP) are commonly used in medium voltage - high power multilevel converters to improve their efficiency. These techniques require solving non-linear equations in order to calculate the firing angles. These equations can be solved by meta-heuristic optimization algorithms, which depend on a set of hyperparameters that must be properly adjusted to ensure their effectiveness. This paper presents a methodology to tune different meta-heuristic algorithms to fairly compare them and select the best algorithm to solve SHE-PWM/OPP formulation. The methodology is applied considering different meta-heuristic algorithms such as genetic algorithm, differential evolution, harmony search and simulated annealing. It has been validated in SHE-PWM and OPP techniques with different number of firing angles.

Medium-voltage high-power converters are usually modulated using low switching frequency techniques such as Selective Harmonic Elimination - Pulse Width Modulation (SHE-PWM) in order to improve their efficiency. To calculate the firing angles, SHE-PWM transcendental equations are usually solved by offline calculation methods, whose computational burden depends on the number of firing angles or eliminated harmonics and the required accuracy in the solutions. Consequently, with the aim of reducing this computational burden, this paper presents an analysis that estimates the maximum accuracy required in the calculated SHE-PWM solutions considering the main non-idealities of the converter. In this sense, a systematic methodology to evaluate the error in the harmonic amplitudes due to control and dead time effects has been developed, providing an optimal error tolerance for the calculation methods that solve the SHE-PWM problem.

The low-speed high automation (LSHA) is foreseen as a development path for new types of mobility, improving road safety and addressing transit problems in urban infrastructures. As these automation approaches are still in the development phase, methods to improve their design and validation are required. The use of vehicle simulation models allows reducing significantly the time deployment on real test tracks, which would not consider all the scenarios or complexity related to automated driving features. However, to ensure safety and accuracy while evaluating the proper operation of LSHA features, adequate validation methodologies are mandatory. In this study a two-step validation methodology is proposed: Firstly, an open-loop test set attempts to tune the required vehicle simulation models using experimental data considering also the dynamics of the actuation devices required for vehicle automation. Secondly, a closed-loop test strives to validate the selected automated driving functionality based on test plans, also improving the vehicle dynamics response. To illustrate the methodology, a study case is proposed using an automated Renault Twizy. In the first step, the brake pedal and steering wheel actuators’ behavior is modeled, as well as its longitudinal dynamics and turning capacity. Then, in a second step, an LSHA functionality for Traffic Jam Assist based on a Model Predictive Control approach is evaluated and validated. Results demonstrate that the proposed methodology is capable not only to tune vehicle simulation models for automated driving development purposes but also to validate LSHA functionalities.

Nowadays, the growing functionalities implemented on vehicles make the simulation phase much more important in the design process. For that purpose, a representative model is required, as it allows to reproduce the exact behaviour of the vehicle, and reduce not only the time required for its setup and testing, but also the cost related to these. Due to this, the development of accurate vehicle models has become one of the main areas of interest for the automotive industry. In this work a 16 DOF (degree of freedom) full vehicle model is presented. This model is based on multibody formulation combined with an appropriate solver for real-time execution. In order to validate this model, data from a real test vehicle has been used, comparing the real dynamic response of the vehicle to the one provided by the developed dynamic model. Results show that the presented approach represents effectively the behaviour of a real vehicle, both in longitudinal and lateral terms.

Intelligent Transportation Systems (ITS) is currently one of the most active research areas, being electric vehicles (EVs) and their vehicle dynamics enhancement key topics. For this purpose, the development of optimal Advanced Driver-Assistance Systems (ADAS) and Advanced Vehicle Dynamics Control Systems (AVDC) is required. Conventionally, these systems have been focused on increasing the stability of the vehicle in critical scenarios. However, EVs enable the possibility of including also the efficiency by making use of the regenerative braking as a control variable. In order to be able to design such sophisticated control systems, it is necessary to implement control techniques capable to manage both stability and efficiency. In this sense, intelligent control techniques have demonstrated to be one of the best alternatives. In this work a Torque Vectoring (TV) algorithm based on intelligent control techniques and with regenerative braking capabilities is presented. The presented TV approach has been implemented in a embedded platform and tested in a Hardware in the Loop (HiL) setup. Results show that the presented approach is able to not only enhance the dynamics vehicle behaviour in a challenging emergency manoeuvre, but also to increase its overall efficiency.

In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.

Nowadays, intelligent transportation systems (ITS) have become one of the main research areas, being electric vehicles (EVs) and automated vehicles key topics. To guarantee safety and comfort and maximize their efficiency, proper vehicle dynamics control systems such as Torque Vectoring (TV) are mandatory. This work proposes an intelligent TV approach for EVs which considers the vertical force distribution among the tractive wheels. This approach allows to maximize vehicle cornering capacity and also its efficiency. In order to demonstrate its effectiveness, its performance is compared using Dynacar High Fidelity vehicle simulator with three traditional approaches found in the literature: PID, Second Order Sliding Mode Control (SOSMC) and Fuzzy Control. Results show that all evaluated controllers improve the handling of the vehicle and the efficiency with respect to the baseline vehicle. However, the proposed intelligent TV system provides better overall results.

Interest in robotic devices for rehabilitation has increased in the last years, due to the increasing number of patients that require rehabilitation therapies, and the need to optimize existing resources. The UHP rehabilitation robot is a multifunctional device that allows to execute robotized therapies for the upper-limb using a simple pantograph based reconfigurable structure and the implementation of advanced position/force control approaches. However, in applications such as rehabilitation, where the robotic device interacts directly with the user, complying with the demands of the users is as important as complying with the functional requirements. Otherwise, the patient will reject the robotic device. Therefore, in this work the pre-clinical validation of the UHP upper-limb rehabilitation robotic platform is presented. 25 subjects of different physical characteristics have participated in the evaluation of the device, evaluating not only the correct behaviour of the device, but also its safety and adaptativity. Results show the correct behaviour of the platform, and a good acceptance rate of the device.

The use of parallel robots has been demonstrated to be an interesting alternative when high accuracy and/or high speed is required. However, in order to achieve these goals, model based controllers are required. This work presents a new model based control approach, the stable Extended CTC, that uses extra data from additional sensors introduced in the passive joints of parallel robot in the controller. The proposed controller guarantees asymptotic stability, which is an important contribution over the previously presented approaches. The use of redundant information increases controller robustness and performance, allowing to reduce tracking error with respect to traditional CTC approaches. The effectiveness of the proposed control law is demonstrated by implementing it in a Delta robot which has been modeled in ADAMS multibody software.

The design of a stable and robust force controller is one of the most important and difficult tasks in rehabilitation robotics. In previous works, the Universal Haptic Pantograph (UHP) was presented as an alternative to conventional arm rehabilitation after a stroke. This robot is composed by a Series Elastic Actuator (SEA) and a Pantograph. In this work an enhanced force control for the UHP is presented. The proposed controller uses the robot model to estimate the contact force without direct measurement and to compensate nonlinearities in the actuators. In order to prove the effectiveness of the approach, several tests are carried out in simulation and experimentally. Results reveal that mean of tracking errors between desired and actual force is smaller than 0.1 N, which is significantly improved compare to that (around 2.5 N) shown in previous results of UHP, indicating that the proposed force control is likely to enhance haptic performance of the UHP.

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.

Industrial devices are increasingly adopting RTOSs or improved versions of general purpose OSs. This fact introduces new possibilities like wrapping the devices with open middleware technologies like CORBA. Thus, by using object oriented interfaces that should be provided by the vendors of the devices, heterogeneous industrial devices could be integrated in distributed applications. However, CORBA-like architectures may result relatively complex for automation engineers. The use of tools that substitute coding tasks for configuration tasks could be beneficial for them. This paper presents an interface for a generic anthropomorphic robotic arm which is being implemented for a particular case and how this interface could be used by an external synchronization service that has been presented in previous works.

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.

This paper analyses the use of the recurrent neural network approach to diagnose degraded cutting regimes in wire electrical discharge machining (WEDM) Process. The main objective of this work is to detect in advance the degradation of the cutting process since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy. Besides, the quantification of the grade of influence of different types of degraded behaviours is meant in this work. In order to achieve all these challenges, a configuration of three Elman neural networks has been selected due to the memorization capability and the dynamic character of the Elman architecture. Each network is dedicated to specific process functions. The results of this work show a satisfactory performance of the presented approach.