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In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes. • The ETANPPAC scheme achieves compliance and prescribed performance with constraints. • Barrier Lyapunov function constrains tracking errors, achieving desired performance. • Event-triggered method reduces the system burden and the Zeno behavior is avoided. • The proposed ETANPPAC scheme is tested on a numerical model and actual manipulator. [ABSTRACT FROM AUTHOR]

In the recent years, the use of artificial intelligence in the education domain has experienced a significant growth. The main reasons for this are the numerous innovative solutions, products and services, which are used not only in the educational processes, but also in the corresponding to them administrative activities. Based on this, the present publication aims to present the latest trends and the applications of the artificial intelligence in the education sector. This research has led to the identification of several key areas, in which the artificial intelligence has a significant contribution to the educational processes, including the adaptive assessment, the intelligent tutoring systems, the educational data mining, the personalized learning, the virtual reality, etc. This publication also presents some of the potential benefits, challenges and ethical considerations, which are associated with the use of the artificial intelligence technologies in the education. [ABSTRACT FROM AUTHOR]

This paper proposes an event-based adaptive tracking control scheme for the n -link robotic systems in the presence of unknown backlash-like hysteresis (BLH) and deferred position constraints. By combining a transformation error with an asymmetric Lyapunov function, the devised control tactic achieves that the position constraints of robotic systems are not violated after user pre-specified time. In contrast to the results of robotic systems with position constraints, this paper removes a common assumption condition generated by the conventional barrier Lyapunov function method. Then, the adverse effect of unknown BLH can be offset by the Nussbaum function. Meanwhile, an event-triggered mechanism is designed to economize on the network bandwidth resources. Finally, based on the Lyapunov theory, an event-based adaptive tracking control tactic is proposed to ensure that all the signals of robotic systems are bounded under unknown BLH and deferred position constraints. Some simulation results proof that the devised control scheme is valid. • A shifting function is designed to remove a common assumption condition. • The Nussbaum function addresses the problem of the unknown backlash-like hysteresis. • The convergence time of robotic systems can be affected by the pre-specified time. [ABSTRACT FROM AUTHOR]

In this work, the trajectory tracking control scheme is the framework of optimal control and robust integral of the sign of the error (RISE); sliding mode control technique for an uncertain/disturbed nonlinear robot manipulator without holonomic constraint force is presented. The sliding variable combining with RISE enables to deal with external disturbance and reduced the order of closed systems. The adaptive reinforcement learning technique is proposed by tuning simultaneously the actor–critic network to approximate the control policy and the cost function, respectively. The convergence of weight as well as tracking control problem was determined by theoretical analysis. Finally, the numerical example is investigated to validate the effectiveness of proposed control scheme. [ABSTRACT FROM AUTHOR]

The industrial zones are increasingly invaded by groups of mobile robots that are the most capable to perform complex tasks by collaborating and cooperating together. The operation of a mobile robot within a dynamic and high-risk environment with strong interaction between robot-robot and human-robot is of a certain complexity of control and safety. Such type of systems requires a safety and hazard investigation to verify if it is able to operate under certain operating conditions, while still ensuring the control and collaboration between mobile robots and human. This paper presents an approach that combines aspects of System-Theoretic Process Analysis (STPA) and Bowtie for safety assessment purposes. The approach we propose is used for a case related to multi-robot systems considering the coordinating, cooperating and collaborating aspects. At first, a risk identification study is done using STPA to extract a set of risk scenarios related to different types of hierarchical coordination architectures in addition to their factors. Afterward, an evaluation of the obtained scenarios is performed by the Bowtie method. The aim of our study is to better compare different control approaches of a multi-agent system. The combination offers detailed hazard identification. It further provides a classification of risks which helps to improve STPA outcomes thus facilitate decision-making over the suitable approach. [ABSTRACT FROM AUTHOR]

In this work, an adaptive controller is designed by an adaptive network called fuzzy rules emulated network (FREN) for a class of nonlinear discrete-time systems when the mathematical model is completely neglected. The characteristic curve between the plant's input and output is drawn by results of the experimental setup. The design parameters are determined by the characteristic curve and utilized to design the controller. The network structure of FREN controller is established by the set of human knowledge-based IF-THEN rules according to the relation between the plant's input and output. The theoretical result is provided to guarantee the convergence of tracking error of the learning algorithm. Furthermore, experimental systems with the LED current control and the robotic system are constructed to validate the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Copyright of Studia Universitatis Vasile Goldis, Physical Education & Physical Therapy Series is the property of Vasile Goldis University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With innovation and aesthetics playing leading roles, the landscape of footbridge design continues to evolve towards lightweight, landmark structures, increasing the emphasis on mitigating lively dynamic responses. To satisfy architectural constraints, auxiliary control devices are being used to control the response under infrequent peak-loading events. Nearly all implemented control devices are permanent installations of passive systems, such as viscous dampers or tuned mass dampers (TMDs), that are tuned to a particular structural property and hence specific to the particular application. Recently, the concept of deployable, autonomous control systems has been presented, in which a robotic platform is combined with an active control device to yield a system that is capable of providing short-term control for a range of structures. This concept is particularly attractive in relation to footbridge applications, where the systems can be deployed during predictable peak-loading events such as marathons or used with temporary footbridges where the need for control depends on the intended use. In this paper, analytical modeling of a prototype system is presented to validate experimental identification. Furthermore, the role of the control-structure interaction (CSI) is described and compensated for through an active controller formulation and the use of a position feedback controller for disturbance rejection. The performance of the prototype system is evaluated experimentally and assessed relative to an equivalent passive TMD device. The experimental study validates the controller formulation and demonstrates the effectiveness of the prototype system. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Librarianship / Turk Kutuphaneciligi is the property of Turk Kutuphaneciler Dernegi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The creation of intelligent control systems by robotic complexes is a priority for the robotics development. It is established that smart systems allow expanding the scope of robots, improving the quality of their work and reducing the operator participation in the decision-making process. In this article, we consider an example of the implementation of an intelligent control system for a robot -manipulator based on the creation of a fundamentally new control system for its electric drives and the use of linguistic variables - the theory of fuzzy logic. The work result is a fundamentally new system of intelligent control of the robot manipulator. Conclusions are made about the prospects for improving the decision making algorithm of the robot control system based on the integration of the Fuzzy control system and the theory of neural networks. [ABSTRACT FROM AUTHOR]

The features of generalized data about the state of the surrounding area forming are considered. These data are formed by means of integrated multisensor airborne system. For example, the means of data integration as the targets grouping and radar image and digital map superposition are presented. [ABSTRACT FROM AUTHOR]

Increasing environmental concerns together with the need for sustainable consumption and production gave birth to the concept of product service-system. This paperwork presents how the concept of product-service systems (PSS), also known as servitization, can increase performance within the value creation chain in the manufacturing environment by deployingits key features within smart manufacturing units to provide them withreconfigurabilityproperties. Highlights on how thekey feature of a product-service system can be used to enable the deployment of reconfigurability within smart manufacturing units towards achieving the concept of reconfigurable manufacturing systems are revealed in the first part of the paper.Considering the new economic models, the ever changing society needs and the effects of disruptive innovations, a business model for manufacturing environmentis further proposed. The business model deploys small scale reconfigurable manufacturing systems by means of smart manufacturing units, intelligent information management platforms, cloud computing services and Internet of Things. An experimental test bench is afterwards presentedin this context. Three similar manufacturing scenarios are considered and reproduced viaa mix ofsix smart manufacturing units and interfaces. The goal of these scenarios isto test the feasibility of servitization towards achieving reconfigurability of smart units.Several industrial equipment among which a dual-arm industrial robot, two electro-mechanical grippers, two electro-mechanical axes and two proximity sensors areintegrated with the embedded system to enable the deployment of PSS features. Also, how servitization is deployed within available smart units, their generic architecture and how they are interconnected is presented in the paper,together with advantages and constraints of the proposed experimental approach. The paper ends with conclusions, remarks regarding the experimental tests and further research directions. [ABSTRACT FROM AUTHOR]

Nowadays, more sophisticated tasks such as teleoperations in surgery are being conducted by robots, which require operators' high-level spatial and team skills. Spatial skills and shared task representations in teams are critical in successfully elaborating decision-relevant information in order to work together seamlessly on complex spatial tasks. In this study, we designed and implemented an innovative remotely controlled robotic system to address this challenge, in addition to a computer simulation system as a control group. We also conducted a set of experiments by randomly assigning 418 participants in teams to both systems in order to examine the effectiveness of the robotic system. The results indicate that the robotic system is as effective as the computer simulation system, and the shared task representations negatively affected training outcomes on robotic training teams. Lastly, we discuss our findings, study implications and future research. [ABSTRACT FROM AUTHOR]

Highlights: [•] We make a full analysis of the robot states by applying observer-based adaptive wavelet neural network tracking control scheme. [•] The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system. [•] We use the adaptive wavelet neural dynamics to tune the weights of the robot parameters and the bounds of the gains of delay states. [•] The stability of the closed-loop system is derived from Lyapunov criterion and Riccati-inequality to achieve H ∞ tracking performance. [•] A three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme. [Copyright &y& Elsevier]

The article discusses trust and deception in human-robot interactions (HRI) as relevant concerns in the design of robots. Discussed are the history of concepts and attitudes toward robots and the factors that affect HRI. Engendering trust in robots is said to require humans retaining significant roles and being informed of the robots' capabilities, limitations, operational environments, reliability, past and current performance as well as the designer's intent and ways to interact.

Copyright of Revista Umbral Científico is the property of Universidad Manuela Beltran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control. [Copyright &y& Elsevier]