Hardware Experiments Research Articles

Force-based organization and control scheme for the non-prehensile cooperative transportation of objects

AbstractOver decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

Neural network adaptive PID-like coupling control for double pendulum cranes with time-varying input constraints

Industrial cranes commonly experience the double pendulum swing effect as a result of a number of reasons, including the magnitude of the cargo and the non-negligible hook mass. Nevertheless, the majority of control strategies for double-pendulum cranes, whether open-loop or closed-loop, are created using linearized dynamics and necessitate accurate knowledge of the model parameters, rendering them susceptible to parametric uncertainties and unmodeled disturbances. To solve these problems, a new neural network adaptive PID-like coupling control method for the nonlinear double pendulum cranes is proposed in this paper. By making full use of the designed composite signal and its integral action, oscillation suppression and precise positioning are realized. In addition, the problem of conventional PID parameter adjustment is solved successfully by determining the PID gains through a neural network adaptive algorithm, automatically. Moreover, it has been shown that the systematic error converges into a compact set around zero for a variety of time-varying actuator failures and saturation situations that can easily occur in practical engineering. Finally, the effectiveness of the proposed control method is verified through hardware experiments, and the comparison results prove the superior performance of the designed controller.

NClSilico: A Closed-Loop neuromodulation platform in silico

The closed-loop electrophysiology is gradually applied to targeted stimulation of nervous system. According to desired neural biomarkers acquired by biological signal measurement, the stimulus pattern can be automatically adjusted with the suitable closed-loop control algorithms. Moreover, the neural computing uses the numerical computing models to simulate the structures, functions and relations of the neurons and the neural networks, as the substitute of biological brain. As the prevalence of the hardware-in-loop (HIL), the HIL closed-loop electrophysiology not only bypasses the problem of biological experiments but also makes up for the insufficient efficiency and real-time performance of software digital simulation. This paper proposes a hardware experiment platform mainly made up of TMS320F28377D digital signal processor (DSP) and AMD Ryzen 5 5600X 6-Core Processor (CPU) for closed-loop electrophysiological research. The hardware platform is programable, portable, real-time and supports the repeated experiments. Closed-loop electrophysiological experiments with different kinds of control algorithms, stimulation waveforms, feedback variables or neuron models can be implemented on the platform, it may promote the development of researches in the field of closed-loop neuroscience and biomedical engineering.

Eliminating the Dependence of GPS or Communication Latency Estimation in Traveling Wave Based Double-Terminal Fault Location

Double-terminal traveling wave-based fault location is one of the most reliable and accurate technologies for power transmission line fault location. However, existing methods require a global positioning system (GPS) or accurate communication latency estimation to synchronize data from the two terminals. In this study, we propose an innovative double-terminal traveling wave-based fault location method that is independent of either GPS or communication latency estimation. The basic principle of the proposed method is that the arrival time difference between initial surges could be roughly calculated through the angle of pre-fault phasors by artificially assigning surge arrival points as the time references. Therefore, an approximate fault distance is obtained. The error could be reduced by detecting the fault-point-reflected surge. Extensive simulation experiments were performed using PSCAD/EMTDC software to assess the performance of the proposed algorithm. The results showed that the proposed method is applicable to different fault circumstances and independent of GPS synchronization and communication latency estimation while maintaining accuracy close to those of state-of-the-art algorithms. Finally, hardware experiments were performed to assess the validity and practicability of the proposed method.

Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter With Full ZVS Range and Optimal Efficiency

Dual active bridge (DAB) converter is the key enabler in many popular applications such as wireless charging, electric vehicle and renewable energy. ZVS range and efficiency are two significant performance indicators for DAB converter. To obtain the desired ZVS and efficiency performance, modulation should be carefully designed. Hybrid modulation considers several single modulation strategies to achieve good comprehensive performance. Conventionally, to design a hybrid modulation, harmonic approach or piecewise approach is used, but they suffer from timeconsuming model building process and inaccuracy. Therefore, an artificial-intelligence-based hybrid extended phase shift (HEPS) modulation is proposed. Generally, the HEPS modulation is developed in an automated fashion, which alleviates cumbersome model building process while keeping high model accuracy. In HEPS modulation, two EPS strategies are considered to realize optimal efficiency with full ZVS operation over entire operating ranges. Specifically, to build data-driven models of ZVS and efficiency performance, extreme gradient boosting (XGBoost), which is a state-of-the-art ensemble learning algorithm, is adopted. Afterwards, particle swarm optimization with state-based adaptive velocity limit (PSO-SAVL) is utilized to select the best EPS strategy and optimize modulation parameters. With 1 kW hardware experiments, the feasibility of HEPS has been verified, achieving optimal efficiency with maximum of 97.1% and full-range ZVS operation.

Hybrid Data-Mechanism Modeling for Power Loss of a Boost Converter

Parameter design is the premise to ensure the optimal performance of power electronic converters, which depends on the accuracy of the established model. However, there is still many issues for improvements in modeling methods for the parameters design of power electronic converters. Due to the lack of key mechanism and the uncertainty of parameters, the existing modeling method shows poor accuracy or efficiency. In order to simplify the process and improve the accuracy of the existing modeling method, this paper proposes a hybrid data-mechanism modeling (HDMM) method for power loss of a typical power electronic converter, Boost converter, which is with UC3842 control chip. Based on the main loss from inductor, switch tube, diode and sampling resistance, the mechanism power loss modeling of Boost converter is built. Besides, the artificial neural network (ANN) is used to learn from the sampled data and train the data-driven model. To improve the accuracy of the mechanism model, the HDMM for power loss of Boost converter is established by modification of data-driven model with limited data. Through the proposed HDMM method, the mechanism modeling method and data-driven method are well combined and the HDMM has the advantage of high accuracy and strong generalization. In the end of this paper, feasibility and accuracy of the proposed HDMM method have been verified by hardware experiments.

Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Bipedal locomotion has been widely studied in recent years, where passive safety (i.e., a biped rapidly brakes without falling) is deemed to be a pivotal problem. To realize safe 3-D walking, existing works resort to nonlinear optimization techniques based on simplified dynamics models, requiring hand-tuned reference trajectories. In this article, we propose to integrate safety constraints into constrained task-space imitation learning, endowing a humanoid robot with adaptive walking capability. Specifically, unlike previous work using nonlinear and coupled capturability dynamics, we first linearize the 3-D capture conditions using appropriate extreme values and then seamlessly incorporate them into constrained imitation learning. Furthermore, we propose novel heuristic rules to define control points, enabling adaptive locomotion learning. The resulting framework allows robots to learn locomotion skills from a few demonstrations efficiently and apply the learned skills to unseen 3-D scenarios while satisfying the constraints for passive safety. Unlike deep enforcement learning, our framework avoids the need of a large number of iterations or sim-to-real transfer. By virtue of the task-space adaptability, the proposed imitation learning framework can reuse collected demonstrations in a new robot platform. We validate our method by hardware experiments on Walker2 robot and simulations on COMAN robot.

A novel distributed primal-dual control algorithm for energy management of DC microgrids considering network constraints

A discrete-time control method is presented for real-time energy management of DC microgrids, where each distributed controller only shares information with its neighbors. Controllers aim to achieve economic dispatch and voltage restoration while maintaining the generator outputs and line currents in their allowable ranges. The proposed control method is developed based on a primal–dual algorithm (PDA) to determine the optimal control variables, in which uncoordinated step-sizes and an adaptive mechanism are introduced to accelerate the algorithm. The convergence analysis is also provided. Finally, numerical tests show the high convergence rate of the algorithm, and the hardware experiments demonstrate the applicability of the proposed control method for real-time energy management.

Multi-ring julia fractal chaotic systems with separated scrolls and nested scrolls

Chaos and fractals are two branches of nonlinear science. However, few scholars have combined chaos and fractals for generating chaotic attractors with different topologies. The main goal of this paper is to combine fractals and chaos to generate separated multi-scroll attractors and nested multi-scroll attractors, as well as to compensate for the lack of randomness and complexity of general chaotic systems. Combining chaotic attractors with two different fractal processes, a class of multi-scroll chaotic attractors with different behaviors are generated, including multi-ring attractors, separated multi-ring attractors, and nested multi-ring attractors. The experiments of spectral entropy (SE) complexity indicates that the proposed multi-ring fractal systems have higher complexity than other chaotic systems. Furthermore, from the perspective of engineering applications, the random performance of one of the multi-ring fractal systems is tested by the NIST test suite. The results demonstrate that the multi-ring fractal system has promising randomness and is well-suited for image encryption applications. Finally, the numerical simulation results are well illustrated by the micro-controller unit (MCU) hardware experiments, which reveal the feasibility of the designed multi-ring attractors, separated multi-ring attractors, and nested attractors.

Physical modeling and simulation of electro-mechanical actuator-based TVC systems for reusable launch vehicles

This paper presents the high-fidelity physical modeling of a Thrust Vector Control (TVC) system operated by Electro-Mechanical Actuators (EMAs) for Reusable Launch Vehicles (RLVs). In contrast to simplified, often linear, models, high-fidelity physical models enable a better assessment of the actual system performance and a deeper verification of the on-board software robustness against disturbances, unmodeled dynamics or degradation. This aspect is essential to enable the reusability concept as driven by long-term reliability requirements. Moreover, critical systems like Fault Detection, Isolation and Recovery (FDIR) logic, whose robustness and efficacy are often difficult to prove, can be thoroughly tested without requiring hardware experiments with eventual invasive modifications for fault injections. This work captures the whole dynamics of the EMA and TVC components, including the power drive electronics, the electrical motor, and the mechanical transmission for the EMA, as well as the mechanical properties of the engine nozzle acting as a load. The resulting differential algebraic equation system is modeled in the Modelica acausal object-oriented modeling language. An ad-hoc framework is implemented to guarantee flexibility and modularity, which allows models with different fidelity levels to be easily exchanged to achieve the simulation objectives and needed accuracy level. The impact of the different models on the closed-loop TVC dynamics is analyzed in both the frequency and time domain, and then benchmarked with a realistic RLV mission. The results show that the high-fidelity physical models provide a better understanding of the more complex effects governing the TVC dynamics and can, in turn, effectively improve its requirement definition process.

Motion planning around obstacles with convex optimization.

From quadrotors delivering packages in urban areas to robot arms moving in confined warehouses, motion planning around obstacles is a core challenge in modern robotics. Planners based on optimization can design trajectories in high-dimensional spaces while satisfying the robot dynamics. However, in the presence of obstacles, these optimization problems become nonconvex and very hard to solve, even just locally. Thus, when facing cluttered environments, roboticists typically fall back to sampling-based planners that do not scale equally well to high dimensions and struggle with continuous differential constraints. Here, we present a framework that enables convex optimization to efficiently and reliably plan trajectories around obstacles. Specifically, we focus on collision-free motion planning with costs and constraints on the shape, the duration, and the velocity of the trajectory. Using recent techniques for finding shortest paths in Graphs of Convex Sets (GCS), we design a practical convex relaxation of the planning problem. We show that this relaxation is typically very tight, to the point that a cheap postprocessing of its solution is almost always sufficient to identify a collision-free trajectory that is globally optimal (within the parameterized class of curves). Through numerical and hardware experiments, we demonstrate that our planner, which we name GCS, can find better trajectories in less time than widely used sampling-based algorithms and can reliably design trajectories in high-dimensional complex environments.

High-dimensional memristive neural network and its application in commercial data encryption communication

With the development of economic globalization, demands for digital business data sharing among different subsidiary companies are constantly emerging. How to achieve secure transmission of commercial data in the Internet of Things (IoT) is a challenging task. In this paper, we propose a memristive neural network-based method for encrypted communication of commercial data. First, a high-dimensional memristive Hopfield neural network (MMHNN) model with three memristive synapses and seven neurons is proposed and its complex dynamical behaviors are deeply analyzed. The results of theoretical analysis and numerical simulations show that the proposed MMHNN can generate quasi-periodic, periodic, chaotic and hyperchaotic attractors, as well as some controllable 1-direction (1D), 2D, 3D hyperchaotic multi-scroll attractors and other types of parametric regulatory dynamics and initial value regulation dynamics. In addition, an analog equivalent circuit for the MMHNN is constructed, and the circuit simulations experimental results demonstrate the correctness and feasibility of the multi-scroll attractors phenomena. Finally, a novel MMHNN-based encryption scheme for IoT commercial data images is proposed by applying hyperchaotic spatial multi-scroll attractors, and the performance analysis shows that the proposed system achieves good encryption performances, especially in terms of keyspace, information entropy, correlation and differential attacks. The hardware experiments further verify the effectiveness and superiority of the proposed commercial data encryption scheme based on the MMHNN.

A fast planning approach for 3D short trajectory with a parallel framework

For real applications of unmanned aerial vehicles, the capability of navigating with full autonomy in unknown environments is a crucial requirement. However, planning a shorter path with less computing time is contradictory. To address this problem, we present a framework with the map planner and point cloud planner running in parallel in this paper. The map planner determines the initial path using the improved jump point search method on the 2D map, and then it tries to optimize the path by considering a possible shorter 3D path. The point cloud planner is executed at a high frequency to generate the motion primitives. It makes the drone follow the solved path and avoid the suddenly appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while reacting quickly to the intruding obstacles.We demonstrate fully autonomous quadrotor flight tests in unknown and complex environments with static and dynamic obstacles to validate the proposed method. In simulation and hardware experiments, the proposed framework shows satisfactorily comprehensive performance.

Control strategy and verification of reducing battery polarization characteristics to improve unmanned aerial vehicles endurance

This study focuses on extending the duration of unmanned aerial vehicles (UAVs) by improving their power systems and introduces an extended endurance system (EES) for UAVs using a combination of lithium-ion batteries (LiBs) and lithium-ion capacitors (LiCs). Subsequently, a novel control strategy is proposed within the EES to enhance the UAV's endurance by improving the LiB's utilization. The EES control strategy incorporates the Lic's utilization to supplement the load with additional energy once the LiB is depleted. This implementation extends the flight duration and surpasses the duration extension resulting from reducing the LiB concentration polarization phenomenon due to the LiC. The hardware experiment and the simulation verify the proposed EES. The results indicate that the extension of UAV's endurance brought about by LiC is limited. Notably, the extension in endurance is primarily attributed to the innovative control strategy implemented within the proposed EES. Due to the LiC, this strategy significantly enhances the LiB's utilization by mitigating the concentration polarization phenomenon associated with it. As a result, the LiB's energy (utilization%) experiences substantial increments of 16.0 %, 25.5 %, and 22.7 % during hovering, altitude variation, and loaded hovering scenarios, respectively.

Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition

As a common interface circuit for renewable energy integrated into the power grid, the inverter is prone to work under a three-phase unbalanced weak grid. In this paper, the instability of grid-connected inverters under the unbalanced grid condition is investigated. First, a dual second-order generalized integrator phase-locked loop (DSOGI-PLL)-based inverter under balanced and unbalanced conditions is modeled. A fourth-order impedance model is established to describe its impedance characteristics under the unbalanced grid condition. To analyze this multi-input multi-output system, a simplified stability analysis method based on the generalized Nyquist stability criterion and matrix theory is proposed. Then, the influences of circuit and control parameters on the stability of the grid-connected inverter system under the unbalanced grid condition are investigated. Finally, the accuracy of the derived frequency-coupled impedance model is verified via simulations, and the effectiveness of the proposed simplified stability analysis method on the system stability analysis is verified via both simulations and hardware experiments.

Event-triggered multi-time scale control and low carbon operation for electric-hydrogen DC microgrid

This paper addresses the escalating need for low-carbon solutions and discusses the integrated control system for the electric-hydrogen DC microgrids. A multi-time scale control mechanism for the DC microgrid is proposed, which aims to enhance its adaptability and scalability. The proposed multi-time control scheme comprises two control layers. The system-level control layer utilizes optimization to address economic issues and carbon trading, aiming to minimize operational costs and carbon emissions in the microgrid. The local-level control layer incorporates an advanced nonlinear control method based on the exponential reaching law-powered terminal sliding mode control (ERL-TSMC) for accurate tracking capabilities at the local-level and to handle dynamic control challenges in real-time. Furthermore, an event-triggered control mechanism is introduced to reduce considerable control efforts while guaranteeing system robustness under various disturbances. Both simulations and hardware experiments demonstrate that the proposed control method guarantees efficient low-carbon operation, achieving a significantly lower average carbon intensity of 0.19 kg CO2/kWh. Additionally, it exhibits robust performance at the local-level, with a steady-state error of only 0.03%.

Inverse-Impedance-Based Centralized Predictive Current Controller for $N$-Parallel Grid-Connected Converters for Circulating Current Elimination

A constant switching frequency centralised predictive grid current controller is proposed for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -parallel grid-connected converters. The proposed work solves the circulating current problem even when converters of different power ratings are paralleled. The circulating current flow is eliminated by equalising the converter terminal voltages of the parallel converters. A grid equivalent model of the converter is mathematically derived using Thevenin's method to achieve the objective. The converters operate at the same power factors with their currents shared, inversely w.r.t. their line inductances/impedances. The method is flexible in applying any modulation method, without contributing to low-frequency circulating currents. Hardware experiments and software simulations validated the inverse-impedance-based current sharing among the parallel converters. Experiment results show quick redistribution of the grid current among the converters under dynamic conditions like incoming/outgoing converter scenarios. The computational requirement with the proposed scheme is independent of the number of converters in parallel. The proposed design is easy to implement and can be deployed for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -parallel converter systems for grid applications like interlinking converters in AC/DC grids, and plug-in type electric vehicle charging stations.

PV Grid-Connected Inverter With DC Voltage Regulation in CCM and VCM Operation to Reduce Switching Losses

In this paper, a photo-voltaic (PV) grid connected inverter (GCI) is employed for multi-functional control (i.e., real power flow control form PV panels, mitigation of current and voltage related power quality (PQ) problems) and the GCI input reference dc voltage is formulated as a function of GCI output currents for loss reduction during low currents through inverter switches. In the latest literature, PV GCI is used for mitigating current related PQ problems, and it is also used for mitigating minor voltage related PQ problems (voltage sag, voltage swell, and voltage imbalance). During voltage sag conditions, GCI output current is high enough to maintain rated voltage at the point of common coupling (PCC) and in this case the reference dc voltage requirement is high. But, during rated grid voltage condition, low power generation from PV, and minimum load condition keeping maximum dc voltage at the input of GCI leads to heavy burden on GCI switches. In the proposed method, in order to reduce losses in GCI, the input dc voltage of GCI is reduced during low GCI output current. The proposed method is validated with MATLAB simulation and hardware experiment by comparing data existing in literature with the proposed method.

Modified VOC Using Three Symmetrical Components for Grid-Supporting Operation During Unbalanced Grid Voltages and Grid-Forming Operation in Hybrid Single-Phase/Three-Phase Microgrid

Virtual Oscillator (VO) control is the most advanced time-domain based control strategy for grid-supporting and grid-forming inverters. VO Controllers (VOCs) provide better dynamic performance than droop controllers and virtual synchronous machine controllers. However, the existing VOCs can only provide synchronization with the positive sequence voltages of a connected electrical network. As a result, the existing VOCs cannot operate in an electrical network with unbalanced voltages. This paper has introduced the Instantaneous Symmetrical component based VOC (S-VOC), which can provide continuous synchronization simultaneously with the positive, negative, and zero sequence voltages of a connected electrical network. The proposed S-VOC enables two very important and relevant functionalities for grid-supporting and grid-forming inverters. The functionalities are the grid-supporting operation in the presence of unbalanced grid voltages and the accurate load-sharing in grid-forming mode when single-phase and three-phase sources are integrated into the same hybrid microgrid architecture. At the same time, the proposed S-VOC preserves the superior dynamic performance of the existing VOC. Furthermore, the proposed S-VOC can replace the existing VOC without any extra sensor. The systematic design procedure and mathematical analysis of the proposed S-VOC are presented in this article. Simulation studies and hardware experiments are conducted for validation.

Implementation of Various Chaotic Spiking Oscillators Based on Field Programmable Analog Array

In this paper, a circuit based on a field programmable analog array (FPAA) is proposed for three types of chaotic spiking oscillator (CSO). The input/output conversion characteristics of a specific element in the FPAA can be defined by the user. By selecting the proper characteristics, three types of CSO are realized without changing the structure of the circuit itself. Chaotic attractors are observed in a hardware experiment. It is confirmed that the dynamics of the CSOs are consistent with numerical simulations.