Hybrid Multi-Servo Motor Controller Within an IoT-Enabled Smart Mechatronics Framework

Controlling naturally ventilated double-skin façade to reduce energy consumption in buildings

Industry operators and plant designers are constantly seeking economic methods to improve plant output, maximize process efficiency and reduce system downtime. This is typically facilitated through industrial automation and control in an industrial network. The industrial network typically consists of a closed network of specialized controllers and sensors with the singular purpose of automation and process control. Machine-to-Machine (M2M) networking can potentially be integrated into the industrial network to expand on the benefits of industrial networking through the access and distribution of additional information. By networking the individual process controllers as well as other relevant sensors and machines a wealth of information can be generated to better model, control and manage the plant. A host of technologies are available to facilitate M2M based on the specific applications and system requirements, allowing both custom and standardized solutions. This report provides a brief overview of M2M in the context of general industry as well as its potential applications in industrial automation and process control.

With the widespread application of robotic arms in industrial automation, and medical and service robots, improving their precise control capabilities has become a technical problem that needs to be solved urgently. Traditional control methods have limitations in dealing with target object deviation and motion accuracy, especially in complex tasks. To solve this problem, this paper proposes an innovative robotic arm motion optimization method, the "somersault" method, which significantly improves the grasping accuracy and motion stability of the robotic arm by decomposing the large-angle rotation of the robotic arm chassis into multiple small-angle continuous rotations. Especially when the target object is located on the negative semi-axis, it can effectively expand the grasping range of the robotic arm. This paper also optimizes the inverse kinematics, spatial solution and PID control algorithms of the robotic arm, and realizes efficient image processing and real-time control through FPGA. Experimental results show that after adopting the "somersault" method, the accuracy and stability of the robotic arm in complex environments are significantly improved, and it can quickly switch between multiple targets and accurately complete the grasping task. This method not only improves the control accuracy of the robotic arm, but also provides a new technical path for future applications in industrial automation, intelligent manufacturing.

Soft grippers, with their high flexibility and environmental adaptability, have shown promising applications in industrial automation, medical assistance, and underwater operations. This study provides a detailed analysis of various actuation methods, such as fluid pressure, mechanical force, electric fields, magnetic fields, and thermal fields, discussing their principles, applications, and limitations. Additionally, the applications of materials such as hydrogels, elastic polymers, shape memory alloys, shape memory polymers, magnetic materials, and liquid metals in soft grippers are summarized, highlighting their respective advantages and disadvantages. On this basis, a comprehensive analysis and outlook on the performance of soft grippers, the challenges they currently face, and their future development directions have been conducted. This article aims to provide comprehensive theoretical insights for the design and application of soft grippers, thus advancing the field.

A vulnerability detection method for IoT protocol based on parallel fuzzy algorithm

A field bus architecture particularly designed for control and sensor applications in industrial automation with high real time and fault tolerance requirements is presented, which not only allows to determine the runtime performance a priori, but also to control it during the entire process duration. Basically, it consists of a dual ring bus, on both its channels telegrams are redundantly transmitted in opposite directions, thus making delay measurements feasible. A GPS-fed, high accuracy ASIC timer module is used for time synchronisation. Only one GPS receiver is required per system, because the bus nodes' timers are synchronised via the field bus by an integrated time measurement procedure. Furthermore, alarm jobs can be handled by the ASIC with highly precise UTC in all units of a distributed system. Security of telegram transfers is achieved by a Hamming encoding, which allows to detect or to correct predeterminable numbers of errors In the data bits, respectively. Here, three protection bits are provided for any four data bits, i.e., for any nibble. In contrast to the prevailing method, viz., that data repetition is requested when an error is recognised, the time required for a corrective action based on Hamming encoding is predictable. Additionally, the transfer of short data nibbles provides the high flexibility needed to deal with different peripheral equipment. Even in the presence of strong disturbances on the transmission channel, the two-way signal coding by a modified frequency shift keying method and decoding by the Goertzel algorithm enables the receivers to recognise the input signals securely In the course of communication, transmitting and receiving modules exchange data and synchronisation characters, which are clearly distinguished by their respective signal encodings. This distinction simplifies identification, and facilitates safe recognition of the two signals. Furthermore, this kind of communication reduces the number of synchronisation characters required.

More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement. Images taken by a digital camera are analyzed, plates and food are located, food type is determined by neural network, distance and angle of food is determined and 3D volume estimated, the results are cross referenced with a nutritional database, and before and after meal photos are compared to determine nutritional intake. We compare against contemporary systems and provide detailed experimental results of our system's performance. Our tracking systems consider the problem of car and human tracking on potentially very low quality surveillance videos, from fixed camera or high flying \acrfull{uav}. Our agile framework switches among different simple trackers to find the most applicable tracker based on the object and video properties. Our MAPTrack is an evolution of the agile tracker that uses soft switching to optimize between multiple pertinent trackers, and tracks objects based on motion, appearance, and positional data. In both cases we provide comparisons against trackers intended for similar applications i.e., trackers that stress robustness in bad conditions, with competitive results.

This paper studies an adaptive event-triggered (AET) control method for sampled-data systems to enhance communication efficiency and conserve resources. Initially, a dynamic event-triggering mechanism is introduced to minimize redundant data transmissions while maintaining system performance. Then, a state feedback control law is designed by optimizing the event-triggered threshold condition, with the system's stability verified using a Lyapunov function. The gain parameters are computed through a cone complementarity linearization algorithm, ensuring computational efficiency and robustness. Additionally, the proposed approach addresses the trade-off between communication resource utilization and control performance by dynamically adjusting the triggering conditions based on system states. This ensures a reduction in redundant transmissions without compromising system reliability. Finally, this method provides a systematic and effective framework for improving communication efficiency, with potential applications in industrial automation and resource-constrained networked control systems.

Today need for deterministic high data rate and high through put communication protocols for security critical applications and safety applications constantly increases. We can find easily many such applications in the automotive industry. As concepts like x-by-wire with x belongs to drive, steer, brake, etc., where steering arms and brake pipes are replaced by electric wires and integrated circuits, more and more popular, robust and fail-safe communication is needed to realize such functionality. Failures of these critical systems will almost immediately lead to severe accidents, personal injury and great loss to the society in all aspects. FlexRay is an option for upgrading existing network systems using CAN in the automotive industry as well as other industrial control applications. It could also be used for new applications in industrial automation, where safety and reliability in a work environment is of utmost importance, due to its deterministic approach to communication of the messages. This is helped by the use of a two channel topology where each channel is able to work independently, but the two channels can also be used to communicate the same information and as such has built in redundancy [1]. This paper will introduce the key features of the FlexRay protocol in automobile and power (Electric) window sliding design and implementation.

This paper presents the development and validation of a custom MATLAB library, designed to facilitate seamless and efficient real‐time communication between the Siemens S7‐PLCSIM Advanced simulator and MATLAB/Simulink. The library uses Siemens.Simulation.Runtime.API to enable this integration and its architecture is structured into three classes—PLCSimAdv, PLCSR, and PLCSW. To demonstrate the library’s capabilities, we have chosen the validation parameters and conducted functional experiments including real‐time communication at the millisecond level, integration and control of a simulated industrial process, and simultaneous operation with multiple virtual controllers. One of the library’s preliminary conditions was to avoid the requests for regenerating of any version of control program or Simulink model as it is presently by S‐functions in TIA portal. The results suggest that the proposed library provides a robust tool for various applications in industrial automation, from the digital twin modelling or simulation of advanced technologies to the operation of traditional control systems. This work has the potential to significantly streamline the simulation, control, and validation of automated processes and opens new ways for research and development in automation and control systems.

Supervisory control systems play a central role in modern industrial automation. However, control theory has recently made significant advances in modeling mixed continuous/discrete event systems (hybrid control systems), whose typical instantiations include the industrial supervisory controller. This article shows how differential Petri nets, a model for hybrid control systems, can be used to represent industrial supervisory systems in a unified way. Typical industrial automation tests can be modeled, whereas the effect of communication protocols and software can be straightforwardly included using conventional Petri nets. Therefore, a global model for the operation of an industrial control system can be formed and its behavior analyzed

Barcode technology plays a crucial role in automatic identification and data acquisition systems, with extensive applications in retail, warehousing, healthcare, and industrial automation. However, barcode images often suffer from blurriness due to lighting conditions, camera quality, motion blur, and noise, adversely affecting their readability and system performance. This paper proposes a multi-scale real-time deblurring method based on edge feature guidance. Our designed multi-scale deblurring network integrates an edge feature fusion module (EFFM) to restore image edges better. Additionally, we introduce a feature filtering mechanism (FFM), which effectively suppresses noise interference by precisely filtering and enhancing critical signal features. Moreover, by incorporating wavelet reconstruction loss, the method significantly improves the restoration of details and textures. Extensive experiments on various barcode datasets demonstrate that our method significantly enhances barcode clarity and scanning accuracy, especially in noisy environments. Furthermore, our algorithm ensures robustness and real-time performance. The research results indicate that our method holds significant promise for enhancing barcode image processing, with potential applications in retail, logistics, inventory management, and industrial automation.

AC servo system has been widely used in many fields with its high performance and the fast development of intelligent control strategy. Targeted at the single-axis motion control applications in industrial automation, a kind of single-axis motion control system, based on FM354 controlled by S7-300 PLC, is designed. The system consists of three to the position, speed and current closed-loop control to achieve precise position control. The position loop is implemented by FM354. The speed loop and current loop are encapsulated in the servo driver. Through monitoring and comparing the dynamic performance with traditional systems, it can find out the bench runs smoothly and positioning accurately, perfectly meeting the technological and application requirements. It also can be applied to a variety of occasions and used for multi-axis extension.

We present the design and implementation of a prototype of Bangla voice controlled robotic motion based on an efficient and unique algorithm for accurately detecting Bangla voice commands. For the detection of Bangla voice commands to control the movement, our developed speech detection system incorporates Mel-Frequency Cepstrum Coefficients based speech feature extraction mechanism and a feature matching technique using Vector Quantization. Our proposed Bangla voice controlled robotic motion can be very effective in rescue operation and disaster management system where a rescuer can dynamically give the rescue robot voiced instructions instead of being stagnant in a place for its controlling. Additional features including dynamic path generating algorithm for the traversed path, capability to accurately follow a specific map to reach a certain destination, detection of different gases and dynamic obstacle avoidance as well as precise position and movement control system add to the flexibility and dimension of the robotic motion. Apart from its possible application in industrial automation, Bangla voice controlled robotic motion can also encourage the view of a robotic machine as a partner rather than a tool by providing a smooth, easy and flexible mean of self-reliant movement for the physically challenged people speaking Bangla language throughout the world.

Sensor fusion and multi-modal perception have evolved beyond simple data combination into dynamic, context-aware systems that fundamentally transform how robots understand their environment. Modern autonomous systems now actively adapt their sensing strategies based on environmental conditions, sensor health, and task requirements. By integrating data from cameras, LiDAR, radar, and inertial measurement units, these systems achieve robust performance even when individual sensors encounter their worst-case scenarios. The evolution of deep learning-based fusion architectures addresses critical challenges in temporal synchronization, drift compensation, and environmental adaptation through dynamic sensor weighting and real-time calibration adjustment. Through edge computing and distributed processing, these innovations enable reliable operation across industrial automation, autonomous navigation, and object tracking applications. The shift from static to dynamic fusion strategies represents a crucial advance in making autonomous systems practical for real-world deployment.