3noopenopenGasparetto A.; Seriani S.; Scalera L.Gasparetto, A.; Seriani, S.; Scalera, L

Alessandro Gasparetto

Lorenzo Scalera

Stefano Seriani

English

Nowadays, the modelling and control of mechatronic and robotic systems is an open and challenging field of investigation in both industry and academia [...

Multidisciplinary Digital Publishing Institute

Modelling and Control of Mechatronic and Robotic Systems

Gasparetto A.

Seriani S.

Scalera L.

Archivio istituzionale della ricerca - Università degli Studi di Udine

applied  sciencesEditorialModelling and Control of Mechatronic and Robotic SystemsAlessandro Gasparetto 1 , Stefano Seriani 2 and Lorenzo Scalera 1,*Citation: Gasparetto, A.; Seriani, S.;Scalera, L. Modelling and Control ofMechatronic and Robotic Systems.Appl. Sci. 2021, 11, 3242. https://doi.org/10.3390/app11073242Received: 30 March 2021Accepted: 2 April 2021Published: 4 April 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Polytechnic Department of Engineering and Architecture, University of Udine, 33100 Udine, Italy;alessandro.gasparetto@uniud.it2 Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy; sseriani@units.it* Correspondence: lorenzo.scalera@uniud.it1. IntroductionNowadays, the modelling and control of mechatronic and robotic systems is an openand challenging field of investigation in both industry and academia [1]. The mathematicalmodelling of a mechanical system is indeed fundamental for the development of experi-mental prototypes. In particular, the kinematic model of a mechatronic or robotic systemis essential for the proper definition of the path and motion law that the system has tofollow during its operation. On the other hand, dynamic models are required to predictboth the forces and torques acting on the system and those required by the actuators. Theseare ultimately useful to simulate scenarios and working conditions of interest. Further-more, the dynamic modelling of a mechatronic or robotic system allows us to evaluateits time-dependent evolution and response under different input conditions. Indeed, aproper model can be implemented to improve the design and performance with differentobjectives, for instance, energy efficiency [2], and vibration reduction [3]. Furthermore, themodelling of a robotic system is essential in path and trajectory planning [4] to enhancecontrol capabilities [5], and to increase kinematic and dynamic performance [6]. Within thisframework, proper control of an automatic system is essential for successfully completinga predefined task. This is especially the case when external disturbances are present, orwhen dealing with flexible systems in which mechanical vibrations have to be considered.2. Modelling and Control of Mechatronic and Robotic SystemsThis Special Issue of the journal Applied Sciences encompasses the kinematic anddynamic modelling, analysis, design, and control of mechatronic and robotic systems,with the scope of improving their performance, as well as simulating and testing noveldevices and control architectures. A broad range of disciplines are included, such as roboticmanipulation, mobile systems, cable-driven robots, wearable and rehabilitation devices,variable stiffness safety-oriented mechanisms, optimization of robot performance, andenergy-saving systems.Several papers deal with control problems, applied to both robotic systems andmechanical vibrations. In paper [7], a novel sliding mode control algorithm is presentedand applied for the trajectory tracking on a robotic manipulator with 3 degrees of freedom(DOF). The proposed controller runs without a precise dynamic model in the presenceof uncertainties and enhances the response, fast convergence time, and accuracy of thetracking position.The main goal of paper [8] is the design, simulation, and experimental verificationof an adaptive feed-forward motion control for a hydraulic differential cylinder. Theproposed solution is implemented on a hydraulic loader crane. Experimental results showthe advantage of the proposed controller in reducing the cylinder position error and theadaptation to model uncertainties.The authors in [9] present the modelling and control of a cable-suspended sling-likeparallel robot for throwing a mass at a suitable time. The mass is carried at the end-effectorby a gripper, which releases the mass so that it can reach a given target point. MathematicalAppl. Sci. 2021, 11, 3242. https://doi.org/10.3390/app11073242 https://www.mdpi.com/journal/applsciAppl. Sci. 2021, 11, 3242 2 of 4models, which also account for the effect of cable elasticity, provide guidelines for planningthe trajectory.In [10], a 8-DOF integrated model of a semi-active seat suspension with a humanmodel over a quarter is presented. A fuzzy logic-based self-tuning PID controller allows toregulate the controlled force on the basis of sprung mass velocity error and its derivativeas input. Simulation results show that the semi-active seat suspension improves theride comfort significantly by reducing the head acceleration compared to passive seatsuspensions. Moreover, in [11], the authors investigate the control of the over-criticalvibration of a transmission shaft system with a device named Smart Spring, an activevibration control mechanism based on piezoelectric material. Simulation results based ona model implemented in ADAMS and MATLAB show the feasibility of the approach inreducing the vibration of the shaft system.Papers [12,13] deal respectively with trajectory optimization and with a novel kine-matic directional index for industrial manipulators. In particular, in [12] a “whip-lashing”method is proposed to optimize the trajectory in order to increase the velocity of individ-ual robot parts, thereby minimizing motion cycle times and exploiting the torque of thejoints more effectively. The efficiency of the method is confirmed by simulation resultson a 5-DOF RV-2AJ manipulator. In [13], a novel method is proposed for evaluating themaximum speed that a serial robot can reach with respect to both the position of therobot and its direction of motion. This approach, called Kinematic Directional Index, isexperimentally applied to a SCARA robot and to an articulated robot with 6-DOF to outlinetheir performances.Paper [14] presents an energy-saving system based on a prefill system and a buffersystem to improve the energy efficiency and the processing performance of hydraulicpresses. Experimental results demonstrate that the proposed system could reduce theinstalled power and pressure shock, increase energy efficiency, and provide the sameprocessing characteristics as the traditional hydraulic press.Several papers in this Special Issue are focused on the design, modelling, and controlof mobile robots and systems. In [15], a hierarchical driving force distribution and controlstrategy for a six-wheel unmanned ground vehicle with independent drive motors ispresented to improve vehicle maneuverability and stability. In [16], authors investigate theeffects of wheel slip compensation in the trajectory planning for mobile tractor-trailer robotapplications. Experimental results on the prototype Epi.q show that with the proposedapproach it is possible to kinematically compensate trajectories that otherwise would besubject to large lateral slip.Papers [17,18] are focused on unmanned aerial vehicles (UAV). In [17], a high-precisionattitude control is proposed for a quadrotor drone equipped with a 2-DOF robotic arm andsubject to varied external disturbances. The research presented in [18] aims to develop anautomatic UAV-based indoor environmental monitoring system for detecting rapid changesin the features of plants growing in greenhouses. Another research work on a mobile systemincluded in this Special Issue is given by [19], which describes a motion planning androbust coordinated control scheme for the trajectory tracking of an underwater vehicle-manipulator system. Model uncertainties, time-varying external disturbances, payload,and sensory noises are considered and evaluated numerically.Other works are focused on legged mobile robots. In [20], a Central Pattern Generatornetwork controller is applied to the trot gait of a quadruped robot. The work of [21]outlines a method for the identification of optimal trajectories of quadruped robots throughgenetic algorithms. Furthermore, the authors in [22] illustrate an optimization of energyconsumption and cost of transport using heuristic algorithms applied to a hexapod robot.The Special Issue also includes interesting papers dealing with mobile robotic systemsfor planetary exploration. Paper [23] presents a general approach to endow a robot withthe ability to sense the terrain being traversed. In [24], the authors propose a novel designof a robotic legged lander based on variable radius drums actuated by cables. Thanks toAppl. Sci. 2021, 11, 3242 3 of 4this device, the robotic system can effectively decelerate with constant force during impactwith ground.Other papers are focused on robotic systems and devices that are designed to phys-ically interact with humans for rehabilitation and safety. Paper [25] presents a novelexoskeleton mechanism for finger motion assistance. The exoskeleton is designed as aserial 2-DOF wearable mechanism that is able to guide human finger motion. The authorsof paper [26] discuss the mechanical redesign of a finger rehabilitation device based ona slider-crank mechanism, starting from an existing prototype. The purpose is to obtaina portable device that can recreate the motion trajectories of a finger. Finally, paper [27]addresses the design and experimental validation of a variable stiffness safety-orientedmechanism for physical human–robot interaction.3. Final RemarksThis Special Issue contains valuable research works focused on the modelling andcontrol of mechatronic and robotic systems, covering a wide area of applications. Thiscollection shows the significant research interest in these topics with a high impact andpotential for future developments. We sincerely hope that this Special Issue on “Modellingand Control of Mechatronic and Robotic Systems” can be a valuable source of informationfor researchers working on these topics. We hope that you enjoy reading this Special Issue!Author Contributions: Conceptualization, A.G., S.S., L.S.; writing—original draft preparation, L.S.;writing—review and editing, A.G., S.S., L.S.; supervision, A.G. All authors have read and agreed tothe published version of the manuscript.Funding: This research received no external funding.Acknowledgments: This Special Issue would not be possible without the contributions of varioustalented authors, hardworking and professional reviewers, and the dedicated editorial team ofApplied Sciences. We thank the authors, who have contributed interesting papers on several subjects,covering many fields of “Modelling and Control of Mechatronic and Robotic Systems”. We wouldlike to take this opportunity to record our sincere gratefulness to all reviewers, whose feedback andsuggestions helped the authors to improve their papers. Finally, we place on record our gratitude tothe publisher and editorial team of Applied Sciences, and special thanks to Wing Wang, SI ManagingEditor from the MDPI Branch Office, Wuhan, for her precious support and help with the publicationof this Special Issue.Conflicts of Interest: The authors declare no conflict of interest.References1. Siciliano, B.; Sciavicco, L.; Villani, L.; Oriolo, G. Robotics: Modelling, Planning and Control; Springer Science & Business Media:Berlin, Germany, 2010.2. Scalera, L.; Carabin, G.; Vidoni, R.; Wongratanaphisan, T. Energy efficiency in a 4-DOF parallel robot featuring compliantelements. Int. J. Mech. Control 2019, 20, 49–57.3. Vidoni, R.; Gasparetto, A.; Giovagnoni, M. A method for modelling three-dimensional flexible mechanisms based on anequivalent rigid-link system. J. Vib. Control 2014, 20, 483–500. [CrossRef]4. Trigatti, G.; Boscariol, P.; Scalera, L.; Pillan, D.; Gasparetto, A. A look-ahead trajectory planning algorithm for spray paintingrobots with non-spherical wrists. In Mechanisms and Machine Science; Springer: Berlin/Heidelberg, Germany, 2019; Volume 66,pp. 235–242.5. Boscariol, P.; Gasparetto, A.; Zanotto, V. Model predictive control of a flexible links mechanism. J. Intell. Robot. Syst. 2010,58, 125–147. [CrossRef]6. Vidussi, F.; Boscariol, P.; Scalera, L.; Gasparetto, A. Local and trajectory-based indexes for task-related energetic performanceoptimization of robotic manipulators. J. Mech. Robot. 2021, 13, 021018. [CrossRef]7. Doan, Q.V.; Vo, A.T.; Le, T.D.; Kang, H.J.; Nguyen, N.H.A. A novel fast terminal sliding mode tracking control methodology forrobot manipulators. Appl. Sci. 2020, 10, 3010. [CrossRef]8. Jensen, K.J.; Ebbesen, M.K.; Hansen, M.R. Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder.Appl. Sci. 2020, 10, 7847. [CrossRef]9. Lin, D.; Mottola, G.; Carricato, M.; Jiang, X. modelling and Control of a Cable-Suspended Sling-Like Parallel Robot for ThrowingOperations. Appl. Sci. 2020, 10, 9067. [CrossRef]Appl. Sci. 2021, 11, 3242 4 of 410. Jain, S.; Saboo, S.; Pruncu, C.I.; Unune, D.R. Performance investigation of integrated model of quarter car semi-active seatsuspension with human model. Appl. Sci. 2020, 10, 3185. [CrossRef]11. Li, M.M.; Ma, L.L.; Wu, C.G.; Zhu, R.P. Study on the vibration active control of three-support shafting with smart spring whileaccelerating over the critical speed. Appl. Sci. 2020, 10, 6100. [CrossRef]12. Benotsmane, R.; Dudás, L.; Kovács, G. Trajectory Optimization of Industrial Robot Arms Using a Newly Elaborated “Whip-Lashing” Method. Appl. Sci. 2020, 10, 8666. [CrossRef]13. Boschetti, G. A Novel Kinematic Directional Index for Industrial Serial Manipulators. Appl. Sci. 2020, 10, 5953. [CrossRef]14. Yan, X.; Chen, B. Energy-Efficiency Improvement and Processing Performance Optimization of Forging Hydraulic PressesBased on an Energy-Saving Buffer System. Appl. Sci. 2020, 10, 6020. [CrossRef]15. Zhang, H.; Liang, H.; Tao, X.; Ding, Y.; Yu, B.; Bai, R. Driving Force Distribution and Control for Maneuverability and Stabilityof a 6WD Skid-Steering EUGV with Independent Drive Motors. Appl. Sci. 2021, 11, 961. [CrossRef]16. Botta, A.; Cavallone, P.; Tagliavini, L.; Carbonari, L.; Visconte, C.; Quaglia, G. An Estimator for the Kinematic Behaviour of aMobile Robot Subject to Large Lateral Slip. Appl. Sci. 2021, 11, 1594. [CrossRef]17. Jiao, R.; Chou, W.; Rong, Y.; Dong, M. Anti-disturbance control for quadrotor UAV manipulator attitude system based on fuzzyadaptive saturation super-twisting sliding mode observer. Appl. Sci. 2020, 10, 3719. [CrossRef]18. Solis, J.; Karlsson, C.; Johansson, S.; Richardsson, K. Towards the Development of an Automatic UAV-Based Indoor Envi-ronmental Monitoring System: Distributed Off-Board Control System for a Micro Aerial Vehicle. Appl. Sci. 2021, 11, 2347.[CrossRef]19. Han, H.; Wei, Y.; Ye, X.; Liu, W. Motion planning and coordinated control of underwater vehicle-manipulator systems withinertial delay control and fuzzy compensator. Appl. Sci. 2020, 10, 3944. [CrossRef]20. Liu, M.; Li, M.; Zha, F.; Wang, P.; Guo, W.; Sun, L. Local CPG Self Growing Network Model with Multiple Physical Properties.Appl. Sci. 2020, 10, 5497. [CrossRef]21. Pepe, G.; Laurenza, M.; Belfiore, N.P.; Carcaterra, A. Quadrupedal Robots’ Gaits Identification via Contact Forces Optimization.Appl. Sci. 2021, 11, 2102. [CrossRef]22. Luneckas, M.; Luneckas, T.; Kriaučiūnas, J.; Udris, D.; Plonis, D.; Damaševičius, R.; Maskeliūnas, R. Hexapod Robot GaitSwitching for Energy Consumption and Cost of Transport Management Using Heuristic Algorithms. Appl. Sci. 2021, 11, 1339.[CrossRef]23. Dimastrogiovanni, M.; Cordes, F.; Reina, G. Terrain Estimation for Planetary Exploration Robots. Appl. Sci. 2020, 10, 6044.[CrossRef]24. Caruso, M.; Scalera, L.; Gallina, P.; Seriani, S. Dynamic modelling and Simulation of a Robotic Lander Based on Variable RadiusDrums. Appl. Sci. 2020, 10, 8862. [CrossRef]25. Carbone, G.; Gerding, E.C.; Corves, B.; Cafolla, D.; Russo, M.; Ceccarelli, M. Design of a Two-DOFs driving mechanism for amotion-assisted finger exoskeleton. Appl. Sci. 2020, 10, 2619. [CrossRef]26. Zapatero-Gutiérrez, A.; Castillo-Castañeda, E.; Laribi, M.A. On the Optimal Synthesis of a Finger Rehabilitation Slider-Crank-Based Device with a Prescribed Real Trajectory: Motion Specifications and Design Process. Appl. Sci. 2021, 11, 708.[CrossRef]27. Ayoubi, Y.; Laribi, M.A.; Arsicault, M.; Zeghloul, S. Safe pHRI via the Variable Stiffness Safety-Oriented Mechanism (V2SOM):Simulation and Experimental Validations. Appl. Sci. 2020, 10, 3810. [CrossRef]

Modelling and control of mechatronic and robotic systems

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Modelling and control of mechatronic and robotic systems

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