Novel hybrid nanogenerators (HNG) with multifunctional materials are gaining immense interest due to their high electrical output and broad applications in the electronic industry. Intrinsic hybridization of single electrode triboelectric (SE‐TE) and piezoelectric (PE) nanogenerators (NGs), that is, double layered (DL)‐HNG can eliminate the real‐time electrical grounding issues of SE‐TE‐NG and produces high output (176.41 V, 168.1 mW m−2) due to synergistic TE–PE effects of Multifunctional Composite film (MFC). The flexible MFC films are made‐up of Cu2O‐doped 0.3Ba0.7Ca0.3TiO3‐0.7BaSn0.12Ti0.88O3 nanoparticles (Cu2O‐BCST NPs), which exhibits morphotropic phase boundary along with the polydimethylsiloxane polymer. Further, DL‐HNG scaled up to 4 × 4 matrix (using the 3D‐printed mask) can work as a flexible self‐powered trackpad (or touch) sensor to control the mouse pointer in computer or touch screen display devices.

Self‐Powered Smart Touchpad using Novel Intrinsic Piezo–Tribo Hybrid Nanogenerator

Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics.

/pdf/will-your-next-therapist-be-a-robot-a-review-of-the-suxzi053.pdf

Will Your Next Therapist Be a Robot?—A Review of the Advancements in Robotic Upper Extremity Rehabilitation

External vibration, shock, unbalanced torque and other uncertain disturbances are mainly transmitted to the motor rotor through the bearing friction. To restrain the uncertain friction disturbances and improve the speed stability of a permanent magnet synchronous motor (PMSM), an optimized active disturbance rejection control (ADRC) algorithm is proposed in this study. Firstly, an auxiliary model of friction and a reduced-order processing method are introduced into extended state observation (ESO) to reduce the burden of single ESO and promote the compensation accuracy of disturbances. In addition, a supervisory radial basis function (SRBF) is employed to supervise and promote the error elimination efficiency of the nonlinear state error feedback rate (NLSEF). The hybrid control algorithm makes up for the deficiency of typical ADRC through the fusion of multiple control quantities. Simulation and experimental results show that the proposed algorithm has strong anti-disturbance performance and effectively solves the problem of low-speed crawling.

/pdf/active-disturbance-rejection-control-for-speed-control-of-9iddovce.pdf

Active Disturbance Rejection Control for Speed Control of PMSM Based on Auxiliary Model and Supervisory RBF

This research presents and compares the outcomes of experimental implementations of different fuzzy logic control structures for a proton exchange membrane fuel cell (PEMFC). These devices are well known for their capability to transform chemical energy into electrical with low emissions. Commonly, a PEMFC has a linkage with a boost converter which allows a suitable end‐user voltage through a nonlinear control law. Hence, the contribution in this sense is the experimental comparison of two fuzzy logic strategies known as type‐1 and type‐2 that were implemented in a PEMFC system. The approaches were embedded in a control board dSPACE 1102 which also has the capability to acquire data. The contrast of results showed capabilities improvement against disturbances in terms of error reduction, control signal, and robustness.

Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

Permanent Magnet Synchronous Motors (PMSMs), with their simple design, small size, and high-power factor, are ideally suited for realizing high-power AC drives and are widely used in various industries. In this study, Fuzzy Active Disturbance Rejection Control (Fuzzy-ADRC) is used to control the speed of the PMSM. When a slight external disturbance occurs, this control strategy maintains the suppression characteristics of the self-excited control for the disturbance and enhances its ability to compensate for the disturbance. First, a mathematical model was developed to study the surface mount PMSM. Then, a motor control simulation model was created using PI control, vector control, and other control methods. The verification results indicate that the improved Fuzzy-ADRC system performs well under both internal and external minor disturbances. It exhibits a faster dynamic response and reduced regulation time (0.026 s to 0.017 s) compared to the traditional ADRC system. Furthermore, it shows less overshoot (reduced from 70% to 2.9%) compared to the Sliding Mode Observer (SMO). Taken together, the improved Fuzzy-ADRC system is characterized by small steady-state error, high load capacity, and control accuracy. With the assistance of this control strategy, the system can track speed with high accuracy and possesses stronger anti-interference capability to mitigate load disturbances. 

Speed Control of PMSM Based on Fuzzy Active Disturbance Rejection Control under Small Disturbances

In this paper, a combined control strategy with extended state observer (ESO) and finite time stable tracking differentiator (FTSTD) has been proposed to perform flexion and extension motion repetitively and accurately in the sagittal plane for shoulder and elbow joints. The proposed controller improves the tracking accuracy, performs state estimation, and actively rejects disturbance. A sinusoidal trajectory as an input has been given to a two-link multiple-input multiple-output (MIMO) upper limb robotic rehabilitation exoskeleton (ULRRE) for a passive rehabilitation purpose. The efficacy of the controller has been tested with the help of performance indices such as integral time square error (ITSE), integral square error (ISE), integral time absolute error (ITAE), and integral of the absolute magnitude of error (IAE). The system model is obtained through the Euler–Lagrangian method, and the controller’s stability is also given. The proposed controller has been simulated for ±20% parameter variation with constant external disturbances to test the disturbance rejection ability and robustness against parametric uncertainties. The proposed controller has been compared with already developed ESO-based methods such as active disturbance rejection control (ADRC), nonlinear active disturbance rejection control (NLADRC), and improved active disturbance rejection control (I-ADRC). It has been found that the proposed method increases tracking performance, as evidenced by the above performance indices.

/pdf/active-disturbance-rejection-control-based-sinusoidal-1imb8gx5.pdf

Active Disturbance Rejection Control Based Sinusoidal Trajectory Tracking for an Upper Limb Robotic Rehabilitation Exoskeleton

Compliance control is crucial for physical human–robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants’ motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.

Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot

A simulation study on oil rigs inspection via virtual reality system using drones aiming to increase the efficiency of the work done during the pandemic era is presented. The usage of drones in virtual reality environment does not only provide sufficient on-site training for the operators, also able to reduce time consumption and labor demands in carrying out inspections and eliminate errors of drone crashing and hazards. Thus, this project covers the construction of virtual simulation framework using drones via Unity 3D, an open-source game engine and multi-rotor drones are selected as the drive mechanism due to its best maneuverability and stability. A 3D model of oil rig is constructed using Unity 3D that consist of equipment such as vessels, tanks, cranes, control rooms and flares like the real-life oil rigs. A 3D model of multi-rotor drone is also developed to produce a dynamic model that can wander around the oil platform. The process of scripting is done in Visual Studio 2019 using C# programming language which is one of the Unity 3D tools, making it essential for modelling and simulation development. Virtual reality features are gained with the presence of virtual headsets, static 3D modelling of oil rigs and dynamic 3D modelling of multi-rotor drones. The use of virtual reality system using drones in oil and gas platform has been successfully integrated to obtain the simulation result.

Drone based Virtual Reality System for Inspection of Oil and Gas Platform

Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments

Effectively tuning a PID controller can be difficult without prior experience or knowledge of the system being controlled. Reinforcement learning is a tool that allows automatic PID tuning with adaptability to environmental change. This technique was utilised for a single degree-of-freedom robot designed for human interaction, proving the validity of the TD3PG algorithm for reference tracking and rehabilitation exercises. These results were measured by the root mean square error of the system and compared to a classical PID controller to determine whether the adaptability improved the system tracking ability. Results showed the classical PID controller resulted in smaller RMSE measurements for a multitude of input signals including sine waves and multi-step functions when the environment remained constant. The adaptive PID controller resulted in smaller RMSE measurements for all input signals when the environment changed to reduce the amount of torque applied to the plant, representing a motor power failure. It is believed that a classic PID controller is better suited for systems with low input frequency and low system uncertainty while adaptive PID controllers are better for systems with changing environments or input signals.

Comparison of Constant PID Controller and Adaptive PID Controller via Reinforcement Learning for a Rehabilitation Robot

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