Modeling and control of dynamic systems that experience abrupt changes are challenging and fundamental tasks in applications of different areas of engineering. Among these, control synthesis for autonomous heavy-duty vehicles has the potential to highly benefit transportation systems. That said, we propose a robust recursive regulator for discrete-time Markov jump linear systems with polytopic uncertain data. We assume the uncertainties affect the state-space parameters as well as the transition probability matrix. We formulate an optimization problem using the penalty function method and design a recursive solution, whereby we attain the robust state feedback gains. We impose no restrictions on how fast the uncertainties can change between two consecutive iterations. Conditions for convergence and mean-square stability are well defined in terms of Riccati recursive equations. We also set up a procedure for powertrain polytopic model identification based on CAN bus data of an autonomous heavy-duty ground vehicle. We provide two numerical examples to verify the effectiveness of the robust recursive regulator. Moreover, in a third example, we validate the obtained powertrain model on a trajectory tracking problem and compute acceleration and braking inputs using the robust regulator.

The dynamic identification of a robot manipulator is a canonical problem in robotics and is usually solved by employing the least squares technique, considering the so-called base parameters of the robot. Such parameters, however, differ from those to which we really want to have access, such as the center of mass and the moments of inertia with respect to this center. Furthermore, the solution obtained by this technique can lead to unsuitable parameters from a physical point of view, which requires a later stage of physical feasibility analysis. In this article, we seek to overcome these drawbacks by identifying the parameters of a 7-DoF lightweight robot using only population-based metaheuristics. We make a study between different techniques and compare them with Bayesian optimization, another black-box approach. We also propose a new algorithm called Probabilistic Differential optimization and show that this algorithm is more efficient than the others considered and can find a physically feasible solution because of its probabilistic exploration ability. We also study the need for more than one excitation trajectory to overcome measurement noise and overfitting, and finally obtain accurate parameters of the Kinova Gen3 arm equipped with a Robotiq 2F-85 gripper.

The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods which demand prior knowledge of the system dynamics. In this case, the control system designer depends on the quality of the uncertain model to obtain an optimal control performance. This work proposes a robust recursive controller designed via multiobjective optimization to overcome these shortcomings. Furthermore, a local search approach for multiobjective optimization problems is presented. The proposed method applies to any multiobjective evolutionary algorithm already established in the literature. The results presented show that this combination of model-based controller and machine learning improves the effectiveness of the system in terms of robustness, stability and smoothness.

Visual-Inertial SLAM methods have become a very important technology for several applications in robotics. This kind of approach usually is composed by sensors as rate gyros, accelerometers and monocular cameras. Magnetometers and GPS modules generally used for outdoors are absent in the SLAM system observation, since the magnetometer measurements deteriorate in the presence of ferromagnetic materials and the GPS module signals are unavailable indoors or in urban environments. In order to make use of all these sensors, we propose Markovian jump linear systems (MJLS) to model the modes of operation of the navigation system based on available sensors and their reliability. An extended Kalman filter for MJLS fuses the sensor data and estimates the motion using the best mode of operation for each particular time instant. Experimental results are presented to show the effectiveness of the proposed method, in situations that would pose a challenge for standard data fusion techniques.

Road traffic crashes have been the leading cause of death among young people. Most of these accidents occur when the driver becomes distracted due to fatigue or external factors. Vehicle platooning systems, as cooperative adaptive cruise control, are one of the results of efforts devoted to the development of technologies for decreasing the number of road crashes and fatalities. Previous studies have suggested that such systems improve up to 273% highway traffic throughput and over 15% of fuel consumption if the clearance between vehicles in this class of roads can be reduced to 2 m. In this paper, we propose an approach that guarantees a minimum safety distance between vehicles taking into account the overall system delays and braking capacity of each vehicle. An l∞-norm robust model predictive controller has been developed to guarantee the minimum safety distance is not violated due to uncertainties on the preceding vehicle behavior. A formulation for a lower bound clearance of vehicles inside a platoon is also proposed. Simulation results show the performance of the approach compared to a nominal controller when the system is subject to both modeled and unmodeled disturbances.

In this paper we present robust information filters for discrete-time Markovian jump linear systems subject to uncertainties in the parameters. We provide recursive estimations to deal with jumps, uncertainties, and unknown initial conditions of the Markovian states. The difficulty in defining initial conditions for this class of systems where the Markov chain is also unknown, justifies the use of information type filters. As an alternative computation method we develop array and fast array algorithms to estimate these uncertain informations. We present numerical examples to demonstrate the efectiveness of the array algorithms proposed. We present also simulation results related with the application of the robust information filter to solve mobile robot localization problems.

This paper presents a new application in the field of rehabilitation robotics. It is part of a research project which consists of the development of a robotic leg prosthesis for above knee amputees. This application aims at providing real-time estimates of the prosthesis foot orientation with respect to ground. An Extended Kalman Filter (EKF) is used in order to estimate the foot orientation based on gyroscope and infrared measurements. Moreover a constraint equation is also included in the EKF to guarantee the constraints in the estimates. Preliminary tests were performed in a platform built for the prosthesis prototype. The results discussed in this paper reveal the feasibility of such technique.

This paper deals with optimal prediction problem for uncertain discrete time singular systems. The parametric uncertainty is assumed to be norm bounded. The singular robust Kalman-type predictor and the corresponding recursive Riccati equation are obtained in their most general formulation where all parameter matrices of the underlying linear model are subject to uncertainties. The quadratic functional developed to deduce this filter combines least squares and penalty functions approaches.

The problem of fault tolerance in cooperative manipulators rigidly connected to a solid object is addressed in this paper. Four faults are considered: free-swinging joint faults, locked joint faults, incorrect measured joint position, and incorrect measured joint velocity. The faults are first detected by a, fault detection and isolation system. Free-swinging and locked joint faults are isolated using artificial neural networks. The other faults are isolated based on the kinematic constraints imposed on the cooperative system. After the isolation of the faults, the control system is reconfigured. Control laws for the system with passive or locked joints are developed. Results of the fault tolerance system applied in simulations and in a real cooperative system are presented.

In this paper, the nonlinear H/sub /spl infin// control via quasi-linear parameter varying (quasi-LPV) representation of the nonlinear system is used to control a underactuated robot manipulator. This technique consists in find a solution to a set of linear matrix inequalities (LMI) satisfying the L/sub 2/ gain generated from the gridding of the state space range. Experimental results obtained from the underactuated robot manipulator UArm II are presented.

The increased utilization of manipulators in hazardous or hard-to-reach environments have led to a corresponding increase on the study of fault tolerant control methods for these mechanisms, ranging from robot design and trajectory planning to fault detection and isolation and post-failure control algorithms. In this article we focus on post-failure control of a mechanical manipulator from the point of view of optimal control, and present a novel method for controlling the positions of the failed, passive joints, in an optimal way. Although the optimization is performed locally, the results indicate the validity and feasibility of the proposed theory.

Usually, fault detection and isolation schemes for robotic manipulators use the system mathematical model to generate the residual vector. However, modeling errors could obscure the faults and could be a false alarm source. In this paper a multilayer perceptron trained with backpropagation algorithm is employed to reproduce the robot input/output behavior generating the residual vector. Then, a radial basis function network is utilized to classify the residual vector generating the fault isolation. Three different algorithms have been employed to train this network. The first employs subset selection to choose the radial units from the training patterns. The second utilizes regularization to reduce the variance of the model. The third algorithm also uses regularization but, instead of one penalty term, each radial unit has an individual penalty term. Simulations employing a two-link manipulator are showed demonstrating that the system can detect and isolate correctly faults that occur in nontrained trajectories.