Sampled-data control of heterogeneous fractional-order (FO) multiagent systems (MASs) under nonidentical denial-of-service (DoS) attacks and time delays is investigated in this study. Based on the received acknowledgments, the designed channel-dependent resilient binary sampled-data scheme adjusts the sampling period following two geometric progressions in the absence and presence of DoS attacks. This provides finer adjustments in sampling periods and ensures a lower average sampling rate compared with traditional periodic sampling schemes. Moreover, the upper and lower bounds of sampling intervals in different channels are heterogeneous, in which only the lower bounds are constrained by inequalities associated with the coefficients of DoS attacks. The impact of DoS attacks is observed from the viewpoint of the overall communication network topology. Observation of the joint recovery time series, defined as those time instants that the joint union of the sampling-based communication graphs under asynchronous DoS attacks can recover to the original topology graph, measures the effectiveness of the designed sampling scheme in restoring the connectivity of communication network under DoS attacks. Heterogeneous memory fusion controllers are used to achieve consensus of FO MASs with time delays based on the distributed asynchronous gradient algorithm. An example is presented to illustrative the validity of the theoretical analysis.

Soft and hyper-elastic materials possess properties of resilience and flexibility, characterizing a class of Soft-Continuum Manipulators (SCM). The latter describes a robot structure with an infinite number of degrees of freedom (DoFs), useful for mobility and manipulation. However, these geometric characteristics are source of modeling and control problems. In this paper, a Pythagorean Hodograph (PH) curve based Reduced-Order-Model (ROM) relying on the Gauss-Lobatto quadrature is investigated for the modeling and the control of SCM. This allows, first, reducing the dimension of the SCM kinematics based on the PH parametric curves with a predefined length and second, developing the shape kinematics control from its control polygon. The use of the Gauss-Lobatto quadrature allows to move independently the PH curve control points, while preserving PH features of length and minimum curve energy. These features are important to control in real-time the shape of the SCM. The proposed approach has been validated numerically and experimentally, carried out on a bio-inspired Soft continuum Elephant Trunk Robot.

This paper proposes a novel mathematical solution to solve the inverse kinematics (IK) of single section mobile continuum manipulators (SSMCMs). Thus, to achieve a given pose of the end-effector (EE), the proposed mathematical solution consists in determining the position and orientation parameters of the mobile platform and of a single section of the continuum manipulator. As advantages, the proposed mathematical solution eliminates the EE pose errors when the dynamic parameters are neglected and the continuum manipulator is cylindrical in shape. A simulation and an experiment validate the proposed approach.

Robotic 3D Concrete Printing (3DCP) is a process of additive manufacturing using building materials. The system that performs 3DCP is a complex system consisting of multiple parts that are independent of each other. However, conventional 3DCP workflows usually lack automatic monitoring of print quality which can be easily affected for various reasons. This paper proposes an integrated workflow of automatic detection of filament deviation in a 3DCP process. The deformation of the filament is adopted as the criterion for print quality evaluation. A Deep Learning-morphology-based filament width estimation method is developed, and a filament deviation detection algorithm with presence of parametric uncertainties is proposed. This workflow allows to detect width deviations in the printed filament by considering several parameters of the printing system. The integrated workflow is implemented and tested through on-site printing tests.

Soft-Continuum Manipulators are of increasing interest to researchers for various non-destructive applications (minimally invasive surgery, fibroscopy, oncology, pipe exploration and many others). They are made with soft material or special arrangement of actuators allowing them to exhibit resilience and dexterity. The concept of Proprioceptive Soft-Continuum Manipulators still remains a major challenge for soft roboticists due to the big issues related to manufacturing process which becomes very expensive including sophisticated or experimental tools, highly skilled technicians and time. However, the manipulator proprioception is very usefull for enhancing the dexterity during their manipulation. Henceforth, this paper investigates a quick and simple approach for the integrated design of a proprioceptive Soft Robotic Bio- Inspired Trunk made with dragon skin 30 Material. This soft manipulator is made up of two segments composed of three independent physical control inputs each. It has an embedded electronics mainly composed of IMUs. The latter have allowed controlling the shape kinematics using a control-oriented modeling approach inspired from the kinematics control of a puppet toy. The developed modeling approach is a Reduced Order Modeling (ROM) which uses Pythagorean Hodograph (PH) curves which lowers in real time, the control dimension of the robot to virtual control points of its representative PH curve. The proposed investigation presents also a comprehensive approach for the manufacturing process of Soft-Continuum Manipulators with complex geometry.

The task space control of robot manipulators requires solving the thorny problem of stabilizing the compound system {outer controller - inner controller - robot manipulator}. To stabilize this compound system, both controllers must be designed by the user to achieve convergence of the tracking error. This problem is tricky to solve in the case of the control of an industrial robot manipulator because its internal controller is not accessible to users. In this work, we propose an adaptive neural network outer controller. The neural networks approximate the dynamics of the inner controller, the kinematic and dynamic parameters of the robot. Besides, the adaptive part finds parameters that achieve the stability of the global system. Since an adaptive approach is sensitive to errors in initial values, we have integrated into the controller a term that constrains the closed-loop system to maintain the prescribed performances. The effectiveness of the approach is demonstrated through Lyapunov's theory, simulation comparisons, and experimental studies.

In this paper, we consider the European Rail Traffic Management System (ERTMS) as a System-of-Systems (SoS) and propose modeling it using colored Petri nets. We formally control the European rail transport, while guaranteeing a set of cross-border security properties. This becomes an essential and challenging task since each of them have mainly developed safety and trackside rules regardless of its neighbors. The feature of this work lies in the approach that considers ERTMS Level 2 as an SoS and addresses the cross-border railway as a mode management problem. In addition, the aspects of mode activation/deactivation, starting state and handling of resource states common to multiple operating modes are taken into account in the proposed model.

In this work, an energy planning strategy is proposed for over-actuated unmanned road vehicles (URVs) having redundant steering configurations. In fact, indicators on the road geometry, the actuation redundancy, the optimal velocity profile, and the driving mode are evaluated for each segment of the URV's trajectory. To reach this objective, a power consumption estimation model is developed for the URV. Due to the presence of unknown dynamic parameters of the URV and uncertainties about its interaction with the environment, an artificial intelligence (AI) technique, based on data-learning qualitative method, is used for the power consumption estimation, namely Adaptive Neuro Fuzzy Inference System (ANFIS). The ANFIS model is obtained using trained data from a Real URV dynamics. Then, an energy digraph is built with all feasible configurations taking into account the kinematic and dynamic constraints based on a 3D grid map setup, according to velocity, arc-length, and driving mode. In this weighted directed graph, the edges describe the consumed energy by the URV along a segment of a trajectory. The vertices describe the start and end points of each segment. Subsequently, an optimization algorithm is applied on the digraph to get a global optimal solution combining driving mode, power consumption, and velocity profile of the URV. The obtained results are compared with the dynamic programming method for global offline optimization. Finally, the obtained simulation and experimental results, applied on RobuCar URV, highlight the effectiveness of the proposed energy planning.

This paper contributes towards multilevel / multiscale graphical modeling of behavior and organization of a set of component systems in a System of Systems concept. The aim of this model is to describe, using same graphical representation, the behavioral properties of physical component systems and organizational aspects of the overall system of systems. We propose a methodology for coupling Hyper Graph, used for modeling the organization of component systems, and Bond Graph, used for modeling the behavior of component systems, in multilevel graphical modeling of system of systems.

Soft robotics is a swiftly growing research area these days. Modeling continuum robots accurately is still a demanding field. The paper aims to propose a shape reconstruction method and the estimation of the kinematic behavior of heterogeneous continuum robot in closed loop kinematic configuration, by using Pythagorean Hodograph (PH) curves. The validation of the model approach has been tested on cooperative continuum robots, namely Compact Bionic Handling Arms (CBHA), driving an intermediate flexible rope (a passive flexible link), by using a 3D tracking system. Experimental comparison of the proposed approach with the existing approaches is performed in terms of accuracy as well as the time cost.

A system of systems (SoS) is a large-scale integrated system. In an SoS, many complex independent systems work collectively for a common mission. Modeling of SoS is challenging due to their large-scale and complexity of the constituent systems. In this paper, generic modeling of a class of SoS, namely mechatronic systems, is proposed based on bond graph modeling approach. Bond graph modeling enables to develop a unified model of SoS, which combines the behavioral and the organizational modeling approaches of SoS. The proposed approach for SoS modeling is applied to intelligent transportation system, where road traffic dynamic in a platoon of intelligent autonomous vehicles (IAVs) is considered for modeling. Finally, the developed bond graph model is simulated for a platoon of four IAVs forming SoS.

This paper addresses the position control of continuum manipulators. Their performances in terms of speed limitation and position accuracy are often mediocre compared with rigid body based robots. In regards to continuum manipulators control, nonadaptive kinematic schemes were shown poor performance in terms of tracking position accuracy, and existing adaptive schemes were time-consuming. This paper presents a novel adaptive control scheme, namely the adaptive support vector regressor controller. The proposed approach exploits the optimization learning methods which yield global solutions of the training problem while keeping small size regressors. These characteristics make it possible to accelerate the convergence of the closed-loop system, thus reducing the execution time. The experimental results obtained using the compact bionic handling assistant robot demonstrate that nonadaptive kinematic architectures even in the presence of accurate learning models are not robust enough to deal with these challenging platforms and that adaptive control schemes can significantly improve the performance.

This paper presents a development of a new method dedicated to the modeling and control of Continuum Robots, based on the Finite Element Method (FEM) using quasi-static assumption. The modeling relies on a discretization of the continuum robots using 6 DoFs Frames along the structure of the robot that is compatible with the modeling of a sequence of rigid vertebras. When the robot's structure relies on rods with constant sections, internal forces are computed with beam elements, placed between two adjacent frames, that applies forces and torques. In the opposite, when the robot is composed of a complex shape deformable backbone separated by the rigid vertebras, a domain decomposition strategy is used to obtain an equivalent stiffness between two vertebras using volumetric FEM. In both cases, for solving the whole robot model and inverting it in real-time, the numerical method takes advantage of the serial nature of continuum robots, using a Block-Tri-Diagonal solver. The factor of improvement in the computation time reaches several order of magnitude compared to a classical FEM model, while keeping a good precision. The method has also been implemented and tested on a real pneumatic CBHA trunk designed by Festo Robotics and some complementarity examples have been generated numerically.

Compact Bionic Handling Assistant (CBHA) is a continuum manipulator, with pneumatic-based actuation and compliant gripper. This bionic arm is attached to a mobile robot named Robotino. Inspired by the elephant's trunk, it can reproduce biological behaviors of trunks, tentacles, or snakes. Unlike rigid link robot manipulators, the development of high performance control algorithm of continuum robot manipulators remains a challenge, particularly due to their complex mechanical design, hyper-redundancy and presence of uncertainties. Numerous studies have been investigated for modeling of such complex systems. Such continuum robots, like the CBHA present a set of nonlinearities and uncertainties, making difficult to build an accurate analytical model, which can be used for control strategies development. Hence, learning approach becomes a suitable tool in such scenarios in order to capture un-modeled nonlinear behaviors of the continuous robots. In this paper, we present a qualitative modeling approach, based on neuronal model of the inverse kinematic of CBHA. A penalty term constraint is added to the inverse objective function into Distal Supervised Learning (DSL) scheme to select one particular inverse model from the redundancy manifold. The inverse kinematic neuronal model is validated by conducting a real-time implementation on a CBHA trunk.

The present paper deals with bond graph model-based for structural component fault detection and isolation. The structural conditions of fault detectability and isolability are obtained directly from the bond graph using the properties of the bicausality and the causal path. It is shown that the monitorability analysis using bond graph is automatically deduced using this unified tool, with respect to the detectability and isolability conditions. A real mechatronic system application of intelligent autonomous vehicle is given to show the efficiency and the simplicity analysis of the proposed approach. This paper was motivated by the problem of integrated design of a fault diagnosis system by considering both, system instrumentation and the set of specifications regarding faults. Existing methods dealing with such problems are based mainly on the existing system instrumentation. In this paper, a fault diagnosis system study and analysis is proposed. This is done by using a unified graphical tool such as Bond Graph tool which is used for system modeling, structural analysis and fault diagnosis conclusions. Therefore, system modeling, fault monitorability analysis, and fault indicator generation are all performed by using the same graphical tool. In addition, the proposed method may be exploited for monitorability analysis before industrial design, i.e., ability to detect and isolate faults with given instrumentation architecture and how to make faulty components monitorable by adding new sensors. To show the effectiveness of the proposed method, an application on real mechatronic system is considered.

Currently traffic management is becoming more important to achieve the goal of sustainable transport, and a good traffic model can describe the traffic behavior efficiently. The traffic models can be classified based on level of details as submicroscopic-, microscopic-, mesoscopic-, and macroscopic-level models. In this paper, we provide a review of the four types of models (submicro, micro, meso, and macro) and then propose a multilevel model of traffic, which combines submicroscopic, microscopic, and macroscopic levels of traffic model. In this work, we do not consider the mesoscopic-level model. At the submicroscopic level, we develop a bond graph model of a four-wheeled vehicle considering the longitudinal, lateral, yaw, and actuator dynamics. At the microscopic level, we develop a car-following model based on virtual interconnections between the submicroscopic bond graph models of vehicles. Then, at the macroscopic level, we deduce macroscopic variables (average speed, density, and flow) from the submicroscopic and microscopic models. Having a multilevel model of traffic allows combining two properties of modeling simulation, one in real-time mode at microscopic and submicroscopic levels and the other at offline mode at macroscopic level. Thus, the whole supervision of the road traffic can be performed. Finally, the multilevel model of traffic is validated on a real-time simulator of vehicle dynamics, based on experimental measurements acquired from intelligent autonomous vehicles (IAVs). In addition, real experiments on IAVs are performed to validate the model.

This paper presents experimental validation in real time of fault detection and isolation and fault tolerant control algorithms for healthy monitoring of an Omni-directional platform, called Robotino@. The latter is composed of three actuated subsystems. The purpose of using fault diagnosis algorithms is to supervise the safe operating of the system, and to study the system reconfigurability strategies in order to ensure that the system remains able to follow a desired trajectory. For such purpose, the fault detectability and isolability is based on analytical redundancy relations. The latter are constraint relations expressing the nominal system behavior and they are written in terms of the measured system variables. Once a fault is detected and the faulty actuated subsystem is determined, the system reconfigurability algorithm analyses the redundancy presented on the former and an appropriate control strategy is applied.

The graphical approaches often have different backgrounds and view a system or an algebraic model from different perspectives in order to facilitate the communication and the understanding. These graphical approaches satisfy the modeling needs and give a clear and easily understandable overview of the behavioral and functional models and make easier to see what the process is, which vulnerabilities and asset that are involved and how the system works. The main goal of this paper is to develop and implement a methodology which combines the functional analysis and the bond graph (BG) tool for intelligent and autonomous systems. As a result, a supervisory interface is obtained, given under a finite automaton, displaying to the operators the possibilities the system has to achieve or not, its objectives. Each operating mode, corresponding to a vertex of the automaton, is associated with a set of services from a functional point-of-view and is defined accurately by a behavioral BG model. Furthermore, the service availability (associated to the BG elements) and the conditions for switching from one mode to another one are analyzed by fault detection and isolation algorithms generated on the basis of the structural and causal properties of the BG tool. Moreover, when a fault is not completely isolable some results can nevertheless be expressed in terms of available or unavailable services.

This paper deals with model-based supervision of a class of system of systems (SoS). The SoS is modeled using hypergraphs, where their architectural representation allows the application of a supervision strategy, from ascending or descending directions directly on the graph. From ascending direction, it is possible to detect local or global faults on a system, when some constraints are no longer satisfied. This is done using the concept of “degree of satisfaction,” where the new operating mode of SoS is deduced using the principle of automata. In the descending direction and based on the constraint satisfaction problem, we can check from the hypergraphs a possible structural reconfiguration of SoS in the presence of faults. This reconfiguration is possible when the available systems satisfy a maximum number of constraints. Finally, an application of a SoS is presented. This is done using intelligent autonomous vehicles in a platoon configuration within a seaport terminal.

A supervision system oversees the operating state of an autonomous vehicle through the availability of the functions and services provided by the vehicle components. However, functional representations do not take into account the system dynamic behaviour and suffer from subjective definitions. For this reason, Bond Graph Models, through their behavioural, structural and causal properties are used to overcome the limitations of functional models. We obtain a new tool taking the form of a finite automaton, to provide fault identification and the reconfiguration conditions of a system. Each operating mode, corresponding to a vertex of the automaton, is associated with a set of services from a functional point of view and is defined accurately by a behavioral bond graph model. Furthermore, the service availability (associated to the Bond Graph elements) and the conditions for passage from one mode to another are analysed by fault detection and isolation algorithms generated on the basis of the structural and causal properties of the bond graph tool. The proposed approach is illustrated by a traction system of an intelligent and autonomous vehicle.

Tire-road is heterogeneous, complex and variable system due to the interaction of several physical phenomenon (mechanical, thermal, hydrodynamic,...). Modelling of such system needs a unified approach using one representation to provide in generic and systematic way the dynamic nonlinear model. The bond graph tool is well suited for this task allowing to estimate the longitudinal impact efforts through the tire-road contact, and to represent overall deformation of the tire in interaction with its environment relating to the longitudinal slip velocity. A practical tool for simulation in modular form is developed under appropriate software allowing the primary validation of the model. Finally, a comparison with experimental results is presented

It is important to estimate the friction force in tire-road contact in order to improve the control performance of a vehicle in critical motions. In this paper, an estimation of a friction force is proposed by using a pressure distribution in 3D. Then, an adaptive control for friction compensation is presented to validate the proposed model.

In some mechanical systems which make very small motions like industrial wagons or constructor robots, the dispositive of their control usually presents some imperfections, a succession of jumps and stops, when the static friction force is larger than the dynamic friction force. This effect is called the stick-slip phenomenon. In this paper, we develop a nonlinear observer in order to estimate the friction force of the contact during the motion, and to compensate the effects which cause the stick-slip phenomenon.