Abstract: This work focuses on the design and development of a multilayer control architecture for a three-wheeled omnidirectional mobile robot, Robotino 4 from Festo. This modular architecture starts with an upper layer where the route to be followed by the robot is planned. The planned trajectory is then passed as a reference to an intermediate layer, where a Model-based Predictive Controller MPC computes the optimal velocity commands to follow the reference trajectory, taking into account the kinematics and the actuator constraints of the robot. Finally, these commands are processed by the lower layer, where three feedback control loops with a PID control law regulate the speed of the three wheels of the robot. To enhance autonomous navigation capabilities, the modularity of the software is analyzed by adding an obstacle avoidance module, as well as a fault detection and fault tolerance modules. In the intermediate layer, different controllers are studied, considering linear and non-linear models. A specific case of the proposed multilayer control architecture is evaluated in MATLAB using a lemniscate trajectory as reference. The simulation includes state estimation errors and motor dynamics, which are experimentally obtained, to better reflect real-world conditions. The results show the MPC capability to track the desired trajectory efficiently, with the added advantage of allowing orientation tracking. This feature offers greater flexibility compared to approaches that assume a constant orientation.

Abstract: Closed-Kinematic Chain Manipulators (CKCM) have gained attention due to their precise Cartesian motion capability through coordinated active joint movements. Furthermore, ensuring synchronization among the joints of CKCMs is critical for reliable operations. An advanced control scheme for CKCMs that combines Nonsingular Fast Terminal Sliding Mode Control (NFTSMC) with Time-Delay Estimation (TDE) while utilizing synchroniza-tion errors, namely Syn-TDE-NFTSMC, to effectively address joint errors in CKCMs was developed in [1, 2]. NFTSMC enables fast convergence through nonlinear terminal sliding while TDE eliminates the need for prior knowledge of the robot’s dynamics, thereby sim-plifying its implementation and reducing its computational requirements. The developed control scheme was rigorously evaluated in [1, 2] using computer simulation and its con-trol performance was compared with those of existing control methods. This paper pre-sents results of an experimental study where the developed control scheme was applied on a real CKCM with 2 degrees of freedom (DOF). Supported by computer simulation study conducted on this manipulator, experimental results show that this control scheme outperformed other existing control schemes in terms of synchronization and control per-formance. The results confirm the efficacy of the developed control scheme in enhancing control precision and system stability, making it a promising solution for improving CKCM control strategies in real-world applications.

Abstract: This paper introduces a configuration and integration model for mobile robots deployed in emergency and special operations scenarios. The proposed method is designed for implementation within the Operational Technology (OT) domain, enforcing security protocols that ensure both data encryption and network isolation. The primary objective is to establish a dedicated operational environment encompassing a command and control center, where the robotic network server resides, alongside real-time data storage from network clients and remote control of field-deployed mobile robots. Building on this infrastructure, operational strategies are developed to enable efficient robotic response in critical situations. By leveraging remote robotic networks, significant benefits are achieved in terms of personnel safety and mission efficiency minimizing response time and reducing the risk of injury to human operators during hazardous interventions. The technologies employed create a synergistic system that ensures data security, encryption, and user interaction through a web-based interface. Additionally, the system includes mobile robots and a read-only application positioned within the DMZ (Demilitarized Zone), allowing for secure data monitoring without granting control access to the robotic network.

Abstract: This paper presents an adaptive strategy to solve the formation control problem for a set of second-order agents with parametric uncertainty and nonlinearity. The strategy regards a group of agents where the nolinearities and uncertainties are represented by a linearly parametrised term, which allows us to consider non-identical agents. In order to ensure the collision-free motion of agents, we propose the use of a repulsive vector field component that is applied only when a pair of agents become nearer than a predefined minimum bound. Numerical simulations were carried out to show the effectiveness of the proposed scheme, first with a simplified example to verify the key features of the control law and a general case to illustrate the performance of the algorithm in a more complex scenario.

Abstract: This study addresses the gap in experimental validation of tilt-rotor vertical take-off and landing (VTOL) UAVs by developing a novel prototype that integrates fixed-wing and multi-rotor advantages. A dynamic model based on the "X" quadrotor configuration was established, and Euler parameters were employed to derive the attitude transformation matrix. Structural optimization using hybrid meshing and inertia release methods revealed a maximum deformation of 57.1 mm (2.82% of half-wingspan) and stress concentrations below material limits (379.21 MPa on fasteners). The landing gear was optimized via a unified objective method, achieving a 33% reduction in equivalent stress. Vibration analysis identified hazardous frequencies (11–12 Hz) to avoid resonance. A PID control system with DSP28377D demonstrated stable motor speed tracking (±5 RPM) and roll attitude control (<10% error). Experimental validation in low-altitude flights confirmed the prototype’s feasibility, though ground effects impacted pitch/yaw performance. This work provides critical experimental data for future tilt-rotor UAV development.

Abstract: To address the performance degradation of standard linear extended state observers (LESO) caused by severe peaking phenomena with increasing system order, which compromises controller accuracy, this paper proposes an innovative output feedback controller using a cascaded observer structure. Through the uniform exponential stability (UES) criterion, we demonstrate that the proposed observer guarantees bounded tracking errors for system states. A novel approach constructs desired load pressure from reference trajectories, effectively decoupling the nonlinear interactions between actual load pressure and input signals. By integrating backstepping control methodology with Lyapunov stability analysis, we develop a robust output feedback controller. Extensive simulations validate the proposed controller's effectiveness, extensive simulations validated the effectiveness of the proposed controller, in all four test scenarios, the tracking errors of the proposed controller were reduced by approximately 10% to 61.5% compared to LESO, regarding external disturbance estimation, the estimation accuracy was approximately twice that of LESO.

Abstract: Robust control of the four-link manipulator arm (FLMA) is an important subject for many industrial applications such as COVID-19 prevention robotics, lower limb rehabilitation robotics and underwater robotics. This study uses the feedback linearized approach to stabilize the complex nonlinear FLMA without applying nonlinear approximator that includes the fuzzy approach and the neural network optimal approach. The article proposes a new approach based on the “first” derived nonlinear convergence rate formula of FLMA to control the highly nonlinear dynamics. The linear quadratic regulator (LQR) method is often applied in the balance controlling space of the underactuated manipulator. This proposed approach takes the place of LQR approach without the necessary trial and error operations. The implications of proposed approach are “globally” valid, whereas the Jacobian linearized approach is “locally” valid. In addition, the main innovation of the proposed method is to perform “simultaneously” additional performances including the almost disturbance decoupling performance that takes the place of the traditional posture-energy approach and avoids some torque chattering behaviour in the swing-up space, and globally exponential stable performance without solving the Hamilton-Jacobin equation. Comparative examples show that the proposed controller is superior to the singular perturbation and the fuzzy approaches.

Abstract: The energy consumption of rotary wing unmanned aerial vehicles has become an important factor restricting their long-term application. This article focuses on decoupling the motion channel and reducing control energy consumption, and proposes a decoupling controller based on dynamic inversion for the complete dynamics of quadcopter unmanned aerial vehicles. Firstly, design a direct closed-loop feedback control for the z-channel to exhibit second-order linear dynamic characteristics with adjustable parameters. Then, the specific functions of pitch angle and yaw angle are combined as virtual control variables for the comprehensive decoupling design of x-direction and y-direction, so that the x-channel and y-channel also exhibit independent parameter adjustable second-order linear dynamic characteristics. Next, by solving the actual control variables, a fast convergence system is dynamically formed by the deviation between the virtual control variables and their actual values, ensuring that the specific function combination of pitch angle and yaw angle quickly converges to the expected value. Finally, the effectiveness and low energy consumption control characteristics of the decoupling control scheme were demonstrated through simulation comparison with other control methods (such as classical PID) in terms of energy consumption.

Abstract: This paper presents a comprehensive investigation into dead reckoning algorithms. The study begins with an in-depth analysis of the fundamental mathematical formulation for navigation position calculations, then introduces the concept of local invariance to refine traditional methods by examining state parameters with inherent stability. Building on this foundation, we propose an efficient iterative optimization algorithm designed specifically for real-time trajectory prediction under sparse sensor data conditions(Only three samples are required). This approach effectively mitigates the impact of data scarcity, enabling robust and accurate trajectory predictions in challenging environments. The proposed method is anticipated to play a pivotal role in following control systems, thereby significantly improving their operational reliability and performance.

Abstract: There are many parts in industrial gluing systems, and the temperature characteristics of these parts vary greatly. In response to this situation, a segmented adaptive PID temperature control method is proposed in this paper. This method combines a segmented temperature control algorithm with a variable control coefficient temperature control algorithm based on output power, which not only ensures that the system has a small overshoot but also ensures that the system has faster convergence speed and better robustness. At the same time, it greatly improves the scope of application and control accuracy of the temperature controller. The experimental results show that, under the same experimental conditions, compared with the traditional PID method, the overshoot of the proposed method is reduced by 2 to 4 degrees Celsius, the convergence speed is increased by about 30%, and the temperature fluctuation amplitude after being disturbed is reduced by about 0.2 degrees Celsius.

Abstract: This work proposes a hybrid deep learning-based framework to visual feedback control an eye-in-hand robotic system. The framework uses an early fusion approach in which real and synthetic images define the training data. The first layer of a ResNet-18 backbone is augmented to fuse interest-point maps with RGB channels, enabling the network to capture scene geometry better. A manipulator robot with an eye-in-hand configuration provides a reference image, while subsequent poses and images are generated synthetically, removing the need for extensive real data collection. The experimental results reveal that this enriched input representation significantly improves convergence accuracy and velocity smoothness compared to a baseline that processes real images alone. Specifically, including feature point maps allows the network to discriminate crucial elements in the scene, resulting in more precise velocity commands and stable end-effector trajectories. Thus, integrating additional, synthetically generated map data into convolutional architectures can enhance the robustness and performance of the visual servoing system, particularly when real-world data gathering is challenging.

Abstract: Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. The proposed model is designed to perform load forecasting, optimizing energy usage in commercial buildings. The CNN block extracts local features, whereas the M-LSTM captures temporal dependencies. The hyperparameter fine tuning framework applied Bayesian optimization to enhance output prediction by modifying model properties with data characteristics. Moreover, to improve occupant well-being in commercial buildings, the thermal comfort adaptive model developed by de Dear and Brager was applied to ambient temperature in the preprocessing stage. As a result, across all four datasets, the BO CNN-M-LSTM consistently outperformed other models, achieving an 8% improvement in mean percentage absolute error (MAPE), 2% in normalized root mean square error (NRMSE), and 2% in R2 score.This indicates the consistent in performance of BO CNN-M-LSTM under varying environmental factors, highlight the model robustness and adaptability.Hence, the BO CNN-M-LSTM model is a highly effective predictive load forecasting tool for commercial building HVAC systems.

Abstract:

This paper presents a new decentralized adaptive control scheme for motion control of robot manipulators built based closed-kinematic chain mechanism (CKCM). By employing the synchronization technique and model reference adaptive control (MRAC) based on the Lyapunov direct method, the Decentralized Adaptive Synchronized Control (DASC) scheme is developed. The DASC scheme can ensure global asymptotic convergence of tracking errors while forcing all active joints to move in a predefined synchronous manner in the presence of uncertainties and sudden changes in payload. In addition, the control scheme has a simple structure that does not depend on the knowledge of the dynamic mathematical model of a robot manipulator resulting in computational efficiency of control scheme implementation. Results of computer simulation conducted to evaluate the performance of the control scheme applied to control the motion of a CKCM manipulator with 6 degrees of freedom are reported and discussed.

Abstract: Given the growing need to enhance the accuracy of exploration robots, this study focuses on designing a teleoperated navigation system for a robot equipped with a continuous track traction system. The goal is to improve navigation performance by developing mathematical models that describe the robot’s behavior, which are validated through experimental measurements. The system incorporates a digital twin based on ROS (Robot Operating System) to configure the nodes responsible for teleoperated navigation. A PID controller is implemented for each motor, with pole cancellation to achieve first-order dynamics and anti-windup to prevent integral error accumulation when the reference is not met. Finally, a physical implementation is carried out to validate the functionality of the proposed navigation system. The results demonstrate that the system ensures precise and stable navigation, highlighting the effectiveness of the proposed approach in dynamic environments. This work contributes to advancing robotic navigation in controlled environments and offers potential for improving teleoperation systems in more complex scenarios.

Abstract: Traditional Proportional Integral and Derivative (PID) controllers are often utilised in industrial control applications due to their simplicity and ease of implementation. However, their performance can be limited in complex, nonlinear, time-delayed systems, as well as in noisy feedback loops. This study introduces Groupers and Moray Eels Optimization (GMEO) with Dual-Stream Multi-Dependency Graph neural network (DMGNN) to optimize PID controller parameters addressing main challenges like nonlinearity, dynamic adaptation to changing conditions, and robust performance under variable operating conditions. The proposed system combines the GMEO algorithm to optimize the PID gains and the DMGNN model to predict and locally adjust these parameters, ensuring improved accuracy and responsiveness. By dynamically tuning the PID parameters based on current system conditions, the system adapts to varying input voltages and load changes, optimizing application performance. The proposed strategy is assessed and contrasted with existing strategies on the MATLAB platform. The proposed system achieves a significantly reduced settling time of 100 ms, ensuring rapid response and stability under varying load conditions. Additionally, it minimizes overshoot to 1.5% and reduces the steady-state error to just 0.005V, demonstrating superior accuracy and efficiency compared to existing methods. These improvements demonstrate the system’s ability to deliver optimal performance while effectively adapting to dynamic environments, showcasing its superiority over existing techniques.

Abstract: Recent years have seen increasing attention on autonomous decentralized microgrids that are disaster-resistant and suitable for local consumption of locally generated renewable energy power. Although various methods have been discussed for achieving microgrids through autonomous decentralized cooperative control, there are few reports that have reached the stage of field testing. In this study, we propose a novel configuration method for DC microgrids, where storage batteries are distributed and directly connected to the DC baseline. We have built a testbed to demonstrate the operation of the DC microgrid through autonomous decentralized cooperative control. Our method simply employs the Droop characteristics inherent in batteries, and we introduce the new concept of a "weakly-coupled grid." This approach allows the realization of microgrids with autonomous decentralized cooperative control without the need for advanced and complex grid control technologies such as that with AI, and with a simple configuration. Additionally, by directly connecting batteries to the baseline, we introduced a grid stabilization method by imparting electrical inertia to the baseline. This paper describes the construction method, the operation principle, and the safe and stable operational methods for autonomous decentralized microgrids using this approach, aiming to serve as a guide for those who wish to build autonomous decentralized controlled microgrids in practice.

Abstract: The shortage of therapists required for the rehabilitation of stroke patients, together with the patients’ lack of motivation in regular therapy, builds the need for a robotic rehabilitation platform. While shared control architectures are studied in literature as means of training, the state-of-art training systems involve a complex architecture and moreover have visible performance limitations. In this paper, a simplified training architecture is proposed, which is particularly targeted for rehabilitation, and also adds the missing features such as complete force feedback, enhanced learning rate, and dynamic monitoring of patient’s performance. In addition to the novel architecture, design of controllers to ensure system stability has been presented. These controllers are analytically shown to meet the performance objectives and maintain system’s passivity. An experimental setup is built to test the architecture and the controllers. A comparison with state-of-art methods is also performed to demonstrate the superiority of the proposed method. It is further demonstrated that the proposed architecture facilitates correcting the inaccurate frequencies at which the patient might operate. This was achieved by defining attribute-wise individual recovery factors to the patient.

Abstract: Cable-driven actuators (CDAs) are extensively used in the rehabilitation field because of advantages such as low moment of inertia, fast movement response, and intrinsic flexibility. However, velocity-induced disturbances pose challenges to accurate force control during dynamic movements. Several strategies for force control have been investigated in the literature, but repetitive time-consuming tests are often required. The aim of this study was to develop a force feedback controller and a speed feedforward compensator for a CDA utilising an experiment-based approach. The CDA comprised a motor, a cable drum, and a force sensor. The plant transfer function was estimated through an open-loop test. A PI force feedback controller was developed and evaluated in a static test. Subsequently, a dynamic test with a constant reference force was conducted, during which the cable was pulled to move at different speeds. The relationship between the motor speed and the cable force was determined, which facilitated further development of a speed feedforward compensator. Additionally, the system dynamics were simulated in MATLAB/Simulink. The static test showed that the PI force controller produced a mean force control error of 4.7 N, which was deemed very good force tracking accuracy. The model simulated the dynamic of CDA with the force output very similar to the experiment (RMSE error of 4.0 N). During the dynamic test, the PI force controller alone produced a force control error of 9.0 N. The additional speed feedforward compensator further reduced this error to 5.6 N. The combined force feedback controller and the speed feedforward compensator produced a satisfactory degree of accuracy during dynamic tests of the CDA at variable speeds. The experiment-based design of the force control strategy for the CDA shows potential to be a control approach for general CDAs, which establishes the foundation for precise movement control as required in cable-driven rehabilitation robotic systems. Future work will be integration of the speed compensator into better feedback algorithms for more accurate force control.

Abstract:

Environmental monitoring refers to the tools and techniques designed to observe an environment, characterize its quality, and establish environmental parameters, to accurately quantify the impact an activity has on an environment. A warmer climate may result in lower thermal efficiency and reduced load-including shutdowns in thermal power plants. It is found in research that a rise in temperature of 1°C reduces the supply of nuclear power by about 0.5% through its effect on thermal efficiency. A drastic change in air pressure also indicates there could be a significant climate change. In the event of a radiological release accident, environmental data is required to reduce radiation exposure to humans. That’s why environmental monitoring is very important for a nuclear power plant. It can be a crucial matter for the industries also because environmental monitoring helps industries operate responsibly, minimize negative impacts on the planet, and contribute to a more sustainable future. An IoT-based system can do environmental monitoring. Anyone using an IoT-based system can get environmental data like temperature, pressure, humidity, etc. Here the projected system delivers sensor data which are got from the environment to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real-time data using graphical information of the environment.

Abstract: This paper presents the design, implementation, and experimental validation of a switched SW FOPID-PID controller for the stabilization of an inverted pendulum (InvP) system. Additional PID and Fractional Order PID (FOPID) controllers were also designed, tuned and validated for comparison purposes. The controllers were tuned offline using Particle Swarm Optimization (PSO) and a mathematical model of the system for simulation. Their performance was assessed through key indicators, including ITAE, ISI, settling time, peak values, and variance, and compared against a manufacturer-provided PID controller. Experimental results demonstrated that all three designed controllers outperformed the manufacturer’s PID under nominal conditions. The SW FOPID-PID controller achieved the best overall performance, balancing control energy efficiency and response quality. Under external disturbances, the FOPID and SW FOPID-PID controllers exhibited superior robustness, being the switched controller the most effective, responding quickly to disturbances while minimizing positional and angular errors.