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This paper aims to design a PD controller for an active suspension system to improve the car’s moving smoothness.

The controller parameters are optimized by an in-loop genetic algorithm (iL-GA). Unlike previous studies that only used conventional GAs to tune coefficients for the controller, the iL-GA designed in this paper provides outstanding efficiency when determining the optimal value range for the system. The optimal value range of parameters is determined by the in-loop algorithm based on criteria related to systematic errors. The optimal values are then calculated by the GA based on this range instead of an uncertain one.

Simulation results show that vehicle body acceleration and displacement values are significantly reduced when using the active suspension system compared to the conventional passive suspension system. The phase difference phenomenon does not occur in the iL-GA situation. In addition, the frequency domain investigation also shows the system’s stability when using iL-GA instead of conventional GA.

To the best of the authors’ knowledge, this is a new application that provides positive effects to the suspension controller. This algorithm can be applied to tune coefficients for direct controllers in the future.

This paper explores the challenges of using cable-driven parallel robots on high-altitude, large-span facades, where redundancy in multicable systems and the elastic deformation of the cables are significant issues. This study aims to improve the accuracy and stability of the work platform through enhanced control strategies. These strategies address the redundancy in multicable systems and reduce the risks associated with cable deformation and mechanical failures during large-span movements.

The paper proposes a dynamic model for a four-rope parallel robot designed explicitly for large-span applications. The study introduces a position–force control strategy incorporating kinematic inverse solutions and a rope dynamics model to account for rope elasticity and its effects. This approach increases the number of system equations to match the unknowns, effectively solving the redundancy problem inherent in multicable systems. In addition, the tension changes of ropes and the stability of the working platform are examined under different motion distances (X = 50 m and X = 100 m) and varying Young’s modulus values (K = 5000 MPa and K = 8000 MPa).

This study’s large-span rope force–position control strategy successfully resolves the typical nonlinear characteristics and external disturbances in multicable parallel systems. By continuously monitoring and adjusting cable tension and end positions, this strategy ensures precise control over each cable’s tension, optimizes the distribution of cable tensions and maintains the system’s stability and response speed. The analysis in this paper indicates that this control strategy significantly improves the motion accuracy of robots operating on large-span high-altitude facades.

Industry adoption: The design and control strategies developed for the four-cable-driven parallel robot can be adopted by companies specializing in facade maintenance, construction or inspection. This could lead to safer, more efficient and cost-effective operations, especially in challenging environments like high-rise buildings. Innovation in robotic solutions: The research can inspire innovation within the field of robotics, particularly in developing robots for specific applications such as large surface maintenance. It showcases how adaptive control and stability can be achieved in complex operational scenarios. Safety improvements: By demonstrating a more stable and precise control mechanism for navigating large facades, the study could contribute to significant safety improvements, reducing the risk of accidents associated with manual facade maintenance and inspection tasks.

This paper combines the force/position hybrid control method with actual robotic applications, offering a novel solution to the complex issue of controlling cable-driven parallel robots in challenging environments. Thus, it contributes to the field. The proposed method significantly enhances the precision and stability of such systems and provides robust technical support for high-precision tasks in complex mechanical settings.

This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

Automatic dependent surveillance-broadcast (ADS-B) is the foundational technology of the next generation air transportation system defined by Federal Aviation Authority and is one of the most precise ways for tracking aircraft position. ADS-B is intended to provide greater situational awareness to the pilots by displaying the traffic information like aircraft ID, altitude, speed and other critical parameters on the Cockpit Display of Traffic Information displays in the cockpit. Unfortunately, due to the initial proposed nature of ADS-B protocol, it is neither encrypted nor has any other innate security mechanisms, which makes it an easy target for malicious attacks. The system is vulnerable to various active and passive attacks like message ingestion, message deletion, eavesdropping, jamming, etc., which has become an area of concern for the aviation industry. The purpose of this study is to propose a method based on modified advanced encryption standard (AES) algorithm to secure the ADS=B messages and increase the integrity of ADS-B data transmissions.

Though there are various cryptographic and non-cryptographic methods proposed to secure ADS-B data transmissions, it is evident that most of these systems have limitations in terms of cost, implementation or feasibility. The new proposed method implements AES encryption techniques on the ADS-B data on the sender side and correlated decryption mechanism at the receiver end. The system is designed based on the flight schedule data available from any flight planning systems and implementing the AES algorithm on the ADS-B data from each aircraft in the flight schedule.

The suitable hardware was developed using Raspberry pi, ESP32 and Ra-02. Several runs were done to verify the original message, transmitted data and received data. During transmission, encryption algorithm was being developed, which has got very high secured transmission, and during the reception, the data was secured. Field test was conducted to validate the transmission and quality. Several trials were done to validate the transmission process. The authors have successfully shown that the ADS-B data can be encrypted using AES algorithm. The authors are successful in transmitting and receiving the ADS-B data packet using the discussed hardware and software methodology. One major advantage of using the proposed solution is that the information received is encrypted, and the receiver ADS-B system can decrypt the messages on the receiving end. This clearly proves that when the data is received by an unknown receiver, the messages cannot be decrypted, as the receiver is not capable of decrypting the AES-authenticated messages transmitted by the authenticated source. Also, AES encryption is highly unlikely to be decrypted if the encryption key and the associated decryption key are not known.

Implementation of the developed solution in actual onboard avionics systems is not within the scope of this research. Hence, assessing in the real-time distances is not covered.

The authors propose to extend this as a software solution to the onboard avionics systems by considering the required architectural changes. This solution can also bring in positive results for unmanned air vehicles in addition to the commercial aircrafts. Enhancement of security to the key operational and navigation data elements is going to be invaluable for future air traffic management and saving lives of people.

The proposed solution has been practically implemented by developing the hardware and software as part of this research. This has been clearly brought out in the paper. The implementation has been tested using the actual ADS-B data/messages received from using the ADS-B receiver. The solution works perfectly, and this brings immense value to the aircraft-to-aircraft and aircraft-to-ground communications, specifically while using ADS-B data for communicating the position information. With the proposed architecture and minor software updates to the onboard avionics, this solution can enhance safety of flights.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

Mobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering.

Building upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output.

Comparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions.

When designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments.

By combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.

This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human skin while maintaining a stable tracking force.

Aiming at the challenge of robots having difficulty tracking changing force trajectories in skin contact scenarios, a single neuron algorithm adaptive proportional – integral – derivative online compensation is used based on traditional impedance control. At the same time, to better adapt to changes in the skin contact environment, a gated recurrent unit (GRU) network is used to model and predict skin elasticity coefficients, thus adjusting to the uncertainty of skin environments.

In two robot–skin interaction experiments, compared with the traditional impedance control and robot force control algorithm based on the radial basis function model and iterative algorithm, the maximum absolute force error, the average absolute force error and the standard deviation of the force error are all decreased.

As the training process of the GRU network is currently conducted offline, the focus in the subsequent phase is to refine the network to facilitate real-time computation of the algorithm.

This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios.

As the proposed approach obtains effective force tracking during robot–skin contact and is verified by the experiment, this approach can be used in robot–skin contact scenarios to enhance the accuracy of force application by a robot.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.