Juan Pérez-Oria

New incremental Takagi-Sugeno state model for optimal control of multivariable nonlinear time delay systems

In this work, a novel approach based on incremental state models has been proposed for the modeling of multivariable nonlinear delayed systems expressed by a generalized version of Takagi-Sugeno (T-S) fuzzy model. One of the key features of the new approach is that the proposed incremental state model compared with the no incremental one, naturally solves the problem of computing the target state, since for a desired output vector, a zero incremental state can be taken as an objective. Moreover, the control action in an incremental form is equivalent to introduce an integral action, thereby cancelling the steady state errors. Among other advantages using incremental models are the disappearance of the affine terms. Then, a fuzzy based linear quadratic regulator (FLC-LQR) is designed. Furthermore, a new optimal observer for multivariable fuzzy systems is developed, because not all states of the nonlinear system are fully available or measured. A multivariable thermal mixing tank system is chosen to evaluate the robustness of the proposed controller. The results obtained show a robust, well damped response with zero steady state error in the presence of disturbances and modeling errors. Graphical abstractDisplay Omitted HighlightsNew incremental state model is proposed for MIMO nonlinear delayed systems.The control action in an incremental form guarantees zero steady state errors.New Optimal observer is proposed, because not all states are available or measured.

Genetically tuned controller of an adaptive cruise control for urban traffic based on ultrasounds

Currently, Adaptive Cruise Controls on the market can only run at high speeds and distances. This makes them useless in urban traffic, where most traffic accidents occur. In the present work, a controller for an adaptive cruise control (ACC) system for urban traffic based on ultrasonic sensors is optimized using Genetic Algorithms. The use of ultrasonic sensors limits their operating range to distances and speeds typical of urban traffic. The proposed system uses the distance between vehicles as measured by the ultrasonic sensor to estimate the relative velocity and acceleration, thus requiring no interaction with the electronics of the car, except for the actuation on acceleration and braking systems. The system is capable of acting on the acceleration and braking systems throughout all its operating range, thereby constituting an additional emergency braking system. With this system both comfort and safety are improved.

Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments

After many years of rigid conventional procedures of production, industrial manufacturing is going through a process of change toward flexible and intelligent manufacturing, the so-called Industry 4.0. In this paper, human–robot collaboration has an important role in smart factories since it contributes to the achievement of higher productivity and greater efficiency. However, this evolution means breaking with the established safety procedures as the separation of workspaces between robot and human is removed. These changes are reflected in safety standards related to industrial robotics since the last decade, and have led to the development of a wide field of research focusing on the prevention of human–robot impacts and/or the minimization of related risks or their consequences. This paper presents a review of the main safety systems that have been proposed and applied in industrial robotic environments that contribute to the achievement of safe collaborative human–robot work. Additionally, a review is provided of the current regulations along with new concepts that have been introduced in them. The discussion presented in this paper includes multi-disciplinary approaches, such as techniques for estimation and the evaluation of injuries in human–robot collisions, mechanical and software devices designed to minimize the consequences of human–robot impact, impact detection systems, and strategies to prevent collisions or minimize their consequences when they occur.

Genetic Optimization of Fuzzy Adaptive Cruise Control for Urban Traffic

The greatest concern in the automotive market is possibly the safety of the occupants of vehicles, pedestrians and other highway users. Cars are increasingly incorporating intelligent systems to improve safety, comfort and energy efficiency.

Shadow-Based Vehicle Detection in Urban Traffic

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

Linear quadratic regulator based Takagi-Sugeno model for multivariable nonlinear processes

In this work, a fuzzy based linear quadratic regulator (FLC-LQR) is developed. The main aim is to obtain an improved performance of non-linear multivariable systems. In this work, the well known weighting parameters approach is applied to optimize local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. A multi-input multi-output (MIMO) thermal mixing process system is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of estimation approach and the proposed controller. The results obtained show a robust, smooth and well damped response using the proposed FLC-LQR of the system under study.

Optimal Control Using Feedback Linearization for a Generalized T-S Model

In this paper, a fuzzy feedback linearization is used to control nonlinear systems described by Takagi-Suengo (T-S) fuzzy systems. In this work, an optimal controller is designed using the linear quadratic regulator (LQR). The well known weighting parameters approach is applied to optimize local and global approximation and modelling capability of T-S fuzzy model to improve the choice of the performance index and minimize it. The approach used here can be considered as a generalized version of T-S method. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the proposed optimal LQR algorithm.

Self-tuning PID controller for autonomous car tracking in urban traffic

In this paper an on line self-tuned PID controller is proposed for the control of a car whose goal is to follow another one, at distances and speeds typical in urban traffic. The best-known tuning mechanism is perhaps the MIT rule, due to its ease of implementation. However, as it is well known, this method does not guarantee the stability of the system, providing good results only for constant or slowly varying reference signals and in the absence of noise, which are unrealistic conditions. When the reference input varies with an appreciable rate or in presence of noise, eventually it could result in system instability. In this paper an alternative method is proposed that significantly improves the robustness of the system for varying inputs or in the presence of noise, as demonstrated by simulation.

Genetically optimized controller for urban traffic emergency braking system based on ultrasonic sensors

In this work, an original controller for an emergency braking system for urban traffic based on ultrasonic sensors is presented, which is optimized by means of a Genetic Algorithm. The main advantage over similar controllers available in the market today, is that in order to maintain a safe distance to the preceding vehicle, it is not necessary to know the speed of either the controlled vehicle or the vehicle ahead. The only information used by the proposed controller is the distance between vehicles, which is measured by an ultrasonic system. In this way, the interaction with the electronics of the vehicle is avoided, except for the action on the braking system. Moreover, the use of ultrasonic sensors also makes possible the detection of pedestrians on the road, resulting in the avoidance of accidents, or at least lessening their consequences.