Reference Generator Research Articles

Determining optimal parameters of algebraic fractals in zero-knowledge authentication protocols

Most information systems, especially on the Internet, have a distributed architecture with remote access and an insecure communication channel. In such systems, the tasks of permanent authorization mean implementing time intervals of user work without re-authentication are especially actual. The problem is that repeatedly sending a password increases the likelihood of it corruption. One solution is to use zero-knowledge protocols. In these protocols, passwords are not transmitted over the channel but are included in the algorithms as parameters. However, the computational complexity, as well as the finite number of passwords, limit their use, ensuring the relevance of further research. Focusing on object of the exchange protocols security, the use of algebraic fractal sets has been proposed as a potentially infinite source of data for passwords. In this work, algorithms were developed, implemented, and tested, which proved the higher reliability of fractal protocols in comparison with the reference generator of random bits (with an error probability of 0.5). It was also noted that the calculation operations have an insignificant influence on the overall time complexity of the exchange protocol as a whole. Practical recommendations for the use of fractals with a Hausdorff dimensionally of about 1.6 on the boundary of the Mandelbrot set are given. The paper also highlights the advantages of including color information in fractal sets, which gives about 3 times improving of confidential security indicators of communication protocol. The proposed algorithms do not require specialized software and can be implemented in the majority of network information systems as an additional module.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Distributed Adaptive Output Feedback Consensus for Nonlinear Stochastic Multiagent Systems by Reference Generator Approach.

This article investigates the distributed leader-following consensus for a class of nonlinear stochastic multiagent systems (MASs) under directed communication topology. In order to estimate unmeasured system states, a dynamic gain filter is designed for each control input with reduced filtering variables. Then, a novel reference generator is proposed, which plays a key role in relaxing the restriction on communication topology. Based on the reference generators and filters, a distributed output feedback consensus protocol is proposed by a recursive control design approach, which incorporates adaptive radial basis function (RBF) neural networks to approximate the unknown parameters and functions. Compared with existing works on stochastic MASs, the proposed approach can significantly reduce the number of dynamic variables in filters. Furthermore, the agents considered in this article are quite general with multiple uncertain/unmatched inputs and stochastic disturbance. Finally, a simulation example is given to demonstrate the effectiveness of our results.

An efficient pre-analysis and optimization generation method for reference curves of automated fiber placement path planning

Complex curved composite components often rely on multiple reference curve algorithms for path planning in automated fiber placement. However, the reference curves are typically manually drawn. Moreover, designing the reference curves follows an iterative planning-analysis-improvement process, which can be inefficient. A new approach for the automatic pre-analysis and optimized generation of reference curves for fiber placement is proposed in this paper to enhance the efficiency of reference curve analysis and generation. Firstly, a pre-analysis algorithm for reference curves based on triangular meshes is proposed. This algorithm analyzes the theoretical geodesic curvature and angular deviation of the path before its planning. Subsequently, a comprehensive evaluation index for reference curve generation is formulated based on the pre-analysis algorithm, and the reference curve is optimized using genetic algorithms. The results demonstrate that the pre-analysis algorithm accurately computes the steering radius distribution of the path. Areas with over-limit steering radius can be eliminated while maintaining angular deviations within 10° by utilizing optimized reference curves for path planning.

Exponential output tracking via a saturated RLC for a class of nonlinear systems under PSD of periodic references

This paper considers the same class of zero-relative-degree nonlinear systems for which a saturated Repetitive Learning Control (RLC) has been recently shown to ensure exponential – and not just asymptotical – convergence to zero of both the output and the input tracking errors in the case of periodic output reference signals with a known single period. The explicit role of the nonlinear unstructured uncertainties is here investigated within the more general scenario in which the output reference signal is multi-periodic. Special emphasis is provided to the generation of the input reference whose effect has to be exponentially nullified by the RLC. In particular, the following question is answered. Restrict the design to the sub-class of periodic output reference signals that admit a Periodic Signal Decomposition (PSD) (namely, the ones that can be written as a finite sum of periodic functions) with pairwise-commensurable periods (so that the knowledge of the multiple periods characterizing the input reference is preserved once the common multiple among the single periods is additionally included). Then, which robustness and convergence properties can be still achieved by the same output-feedback (definitely saturated) RLC, once it is intuitively generalized to transiently include an additional bank of learning estimation schemes corresponding to the single periods? Interestingly, the theoretical tools of this paper can be also used to successfully address identifiability and convergence issues regarding the identification of periodic components of general multi-periodic signals.

ADRC‐based symmetric phase‐locked loop structure for improving low‐frequency stability of grid‐connected inverters

AbstractWith a small short‐circuit ratio (SCR), the grid‐connected inverter is prone to low‐frequency oscillation instability due to the dynamic interaction between the phase‐locked loop (PLL) and the weak grid. To this end, an active‐disturbance‐rejection‐controller‐based symmetric PLL (ADRC‐based SyPLL) is proposed in this article to simplify the system modelling and improve the low‐frequency stability of the inverter system. Specifically, the ADRC technique is applied to phase‐regulated feedback control of a PLL, where an extended state observer (ESO) is used to estimate the lumped disturbance of the controlled system. A control loop symmetric to the q‐axis is further designed on the d‐axis of the PLL, and the d‐axis information is then added at the reference generation side to eliminate the coupled frequency term in the small‐signal model. A simple and accurate single‐input single‐output (SISO) model is thus obtained. The impedance stability analysis for grid‐connected inverters shows that the proposed ADRC‐based SyPLL can significantly improve the low‐frequency stability margin of the system compared with the conventional PI‐regulated PLL. Finally, the experimental results validate the effectiveness and superiority of the proposed method.

A Multi-Channel Data Simulator Based on the Time Unification System

In satellite and airborne electro-optical tracking systems, there are numerous processing devices and complex data flows. To ensure the coordinated operation of the system, the multiple devices within the target system must operate under unified time control for data acquisition, computation, and output. This study introduces a multi-channel data simulator based on a time unification system. The complete simulation system includes the host computer, simulator, target system, and time reference generator. The simulator has programmable input and output interfaces for multi-channel protocols and has storage and real-time working modes. In the storage mode, the simulated data are pre-transmitted to the simulator’s storage and sent to the target system according to the time reference generator. The simulator simultaneously stores the target system results. In the real-time mode, the host computer generates simulated data based on the target system’s results and outputs the data through the simulator in real time. The main contribution of the simulator is that it achieves system-level closed-loop simulation and completes the functional and performance verification of the target system. Through experimental verification, it is found that the simulator can achieve 4.2 Gbps of simulated data transmission and 1.6 Gbps of data reception and storage, with a closed-loop delay of 39.9 µs.

Simulation and Analysis of Dynamic Voltage Restorer with PI Controller under Different Fault Condition

Abstract: This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an IEEE conference. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text. The demand for quality power has become a challenging issue for industrial area and consumers. Among them voltage unbalance is considered as the major affecting problem leads to degradation in performance of electrical equipments. FACTS devices used for compensation are the best method to overcome such problem. Among them DVR considered the most efficient and cost effective. In a control system of the DVR main consideration includes: detection of the start and finish of the sag, voltage reference generation, transient and steady-state control of the injected voltage, and protection of the system. In this work, the simulation model of nonlinear load connected power distribution system with Dynamic voltage Restorer controlled by PI Controller has been developed using Matlab/Simulink. In this case we simulate three fault likewise: Three-line to ground fault, Double-line to ground fault and Single-line to ground fault with two different PI controller to study the THD.

Design of an SMA-Based Actuator for Replicating Normal Gait Patterns in Pediatric Patients with Cerebral Palsy.

Cerebral Palsy refers to a group of incurable motor disorders affecting 0.22% of the global population. Symptoms are managed by physiotherapists, often using rehabilitation robotics. Exoskeletons, offering advantages over conventional therapies, are evolving to be more wearable and biomimetic, requiring new flexible actuators that mimic human tissue. The main objective behind this article is the design of a flexible exosuit based on shape-memory-alloy-based artificial muscles for pediatric patients that replicate the walking cycle pattern in the ankle joint. Thus, four shape-memory-alloy-based actuators were sewn to an exosuit at the desired actuation points and controlled by a two-level controller. The loop is closed through six inertial sensors that estimate the real angular position of both ankles. Different frequencies of actuation have been tested, along with the response of the actuators to different walking cycle patterns. These tests have been performed over long periods of time, comparing the reference created by a reference generator based on pediatric walking patterns and the response measured by the inertial sensors. The results provide important measurements concerning errors, working frequencies and cooling times, proving that this technology could be used in this and similar applications and highlighting its limitations.

Precision motion control of rotary flexible link manipulators using polynomial input trajectories and feedback control

This paper aims to develop a high-precision controller for a flexible link manipulator using polynomial reference, feedforward input, and feedback control. The control objective is to move the tip of the flexible link to a target position as quickly as possible without exciting its resonant modes. Upon a frequency response-based system identification, a frequency-domain feedback controller is designed for reference tracking. The controller includes a proportional-integral controller, a lead filter, and a set of notch filters to control the rigid body and the flexible link dynamics. A set of polynomial reference and feedforward input trajectories are then designed to improve the tracking performance and minimize residual vibrations. The conventional polynomial reference is generated based on the rigid body motion of the system. A revised polynomial reference generation method is then developed by adding flexible motions to the conventional reference. Simulations and experimental tests indicate significant improvements in the performance of the control system using the revised polynomial trajectories.

Event-Triggered Consensus of Uncertain Euler-Lagrange Multiagent Systems Over Jointly Connected Digraphs.

In this article, a fully distributed event-triggered protocol is proposed to solve the consensus problem of uncertain Euler-Lagrange (EL) multiagent systems (MASs) under jointly connected digraphs. First, distributed event-based reference generators are proposed to generate continuously differentiable reference signals via event-based communication under jointly connected digraphs. Unlike some existing works, only the states of agents rather than virtual internal reference variables need to be transmitted among agents. Second, adaptive controllers are exploited based on the reference generators so that each agent can track the reference signals. The uncertain parameters converge to their real values under an initially exciting (IE) assumption. It is proved that the uncertain EL MAS achieves state consensus asymptotically under the proposed event-triggered protocol composed of the reference generators and the adaptive controllers. A unique feature of the proposed event-triggered protocol is its fully distributed property: the protocol does not depend on global information about the jointly connected digraphs. Meanwhile, a minimum interevent time (MIET) is guaranteed. Finally, two simulations are conducted to show the validity of the proposed protocol.

Segment-compensated Bandgap Reference Generator with Low-temperature Drift

Abstract In this paper, a segment-compensated low-temperature-drift bandgap reference generator is proposed and designed to get the high-order nonlinear temperature characteristics for the bandgap reference circuit. Based on the Nuvoton 0.35 μm BCD process, the circuit is simulated and analyzed. The result is that a temperature coefficient of 1.12 ppm/℃ over the range of -40℃ to 125℃ is obtained, which is much lower than the 6.88 ppm/℃ before compensation. It is demonstrated that the power supply rejection ratio is 141.3 dB at 1, 000 HZ, low-frequency open-loop gain is 75 dB, and a phase margin is 57°.

Development and Validation of the High-Voltage Direct-Current Modular Multilevel Converter (HVDC-MMC) Model for Converter Transformer Protection Studies.

The electrical protection of power networks with fault contribution from inverter-based power sources imposes new application challenges that have to be dealt with by protection engineers. This paper describes the development of a study case model of an HVDC-MMC link for testing the protection behaviour of connected converter transformers. The paper summarises the implementation and validation of the converter control as well as enhancements to provide Fault Ride-Through capability and fast fault current injection as required by the German Technical Connection Rules for HVDC. The grid code standard requires positive- and negative-sequence reactive current injection in the case of grid faults. A Doubled Decoupled Synchronous Reference Frame Phase Locked Loop (DDSRF-PLL) for Vector Current Control (VCC) is implemented. Additionally, a Fault Detection and Fault Ride-Through Reference Generator with a Current Limitation strategy is introduced. Though these techniques are well described in the literature, the DDSRF is improved for current control stability. The relationship between the parameters of the PLL and the control, as well as the behaviour of the protection system, are demonstrated. Grid faults with large voltage dips pose a significant challenge to the stability of the control system. Nevertheless, it is shown that with the developed model, it is possible to make general statements about the protection behaviour in an inverter-based environment.

A Simplified Power Regulation Strategy for Grid-tied PMSG-based Marine Current Turbine System in High-speed Marine Current Conditions

Marine current turbine systems (MCTSs) play a crucial role in harnessing renewable energy from ocean currents. These systems are designed with specific rated values of marine current speed, dictating their optimal operational parameters. However, the dynamic nature of marine currents can lead to fluctuations, occasionally exceeding the rated values for which these systems are designed. This poses significant challenges to the efficient and safe operation of MCTSs, necessitating the development of effective power regulation strategies. In response to these challenges, this paper presents a simplified power regulation strategy to address the complexities of high-speed marine current conditions. The strategy focuses on constraining the reference torque command, a pivotal parameter governing power generation within MCTS. By precisely managing the reference torque command, the strategy aims to ensure that power output remains within designated limits, thereby safeguarding the MCTS despite high-speed marine current conditions. The proposed strategy leverages a computational approach to determine the generator's reference torque command, utilizing key parameters such as the rated power of the MCT and the mechanical angular rotor speed. It facilitates seamless transition between unconstrained reference torque command (URTC) and constrained reference torque command (CRTC) modes, enabling precise power regulation. This flexibility empowers the MCTS to respond promptly to changes in marine current conditions, facilitating precise and responsive power regulation. To validate the effectiveness of the proposed strategy, the paper conducts a comprehensive analysis of MCTS performance across both URTC and CRTC modes utilizing Typhoon HIL604 real-time simulator. Through detailed examinations of MCT speed responses, generator torque, output power, and grid-side parameters, the study provides valuable insights into the dynamics of MCTS operation. This comprehensive understanding enhances the stability and adaptability of MCTSs across diverse operating conditions, thereby advancing their potential as a sustainable energy solution.

Virtual control contraction metrics: Convex nonlinear feedback design via behavioral embedding

AbstractThis article presents a systematic approach to nonlinear state‐feedback control design that has three main advantages: (i) it ensures exponential stability and ‐gain performance with respect to a user‐defined set of reference trajectories, (ii) it provides constructive conditions based on convex optimization and a path‐integral‐based control realization, and (iii) it is less restrictive than previous similar approaches. In the proposed approach, first a virtual representation of the nonlinear dynamics is constructed for which a behavioral (parameter‐varying) embedding is generated. Then, by introducing a virtual control contraction metric, a convex control synthesis formulation is derived. Finally, a control realization with a virtual reference generator is computed, which is guaranteed to achieve exponential stability and ‐gain performance for all trajectories of the targeted reference behavior. We show that the proposed methodology is a unified generalization of the two distinct categories of linear‐parameter‐varying (LPV) state‐feedback control approaches: global and local methods. Moreover, it provides rigorous stability and performance guarantees as a method for nonlinear tracking control, while such properties are not guaranteed for tracking control using standard LPV approaches. Code is available at https://github.com/ruigangwang7/VCCM.

Design of 2DOF control system fused with artificial intelligence for power enhancement and mitigation of degradation in fuel cell systems

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model.

Internal Model-Based Robust Path-Following Control for Autonomous Vehicles

The paper presents a new internal model control (IMC) based control technique for lateral trajectory tracking of autonomous vehicles. The controller’s proposed structure employs a robust, fault-tolerant nonlinear internal servo control with optimal reference generation concerning vehicle yaw stability and physical limitations. The presented inscription of the reference generation creates a convex optimization task that can be used in real-time applications. Improvements in yaw-rate stability of vehicle motion control are first shown through simulation results performed in a Simulink environment. The controller structure was also implemented in a real-time model and was examined in a Mercedes C-Class vehicle. In this article, the simulation results and the real-time measurements are presented. The results show that the proposed controller has high efficiency in disturbance rejection and lower sensitivity towards parameter changes compared to a model predictive control (MPC) structure.

Neural Network With Cloud-Based Training for MTPA, Flux-Weakening, and MTPV Control of IPM Motors and Drives

An interior permanent magnet (IPM) motor is a prime electric motor used in electric vehicles (EVs), robots, and electric drones. In these applications, maximum torque per ampere (MTPA), flux-weakening, and maximum torque per volt (MTPV) techniques play a critical role in the efficient and reliable torque control of an IPM motor. Although several approaches have been proposed and developed for this purpose, each has its specific limitations. The objective of this paper is to develop a neural network (NN) method to determine MTPA, flux-weakening, and MTPV operating points for the most efficient torque control of the motor over its full speed range. The NN is trained offline by using the Levenberg-Marquardt backpropagation algorithm, which avoids the disadvantages associated with online NN training. A cloud computing system is proposed for routine offline NN training, which enables the lifetime adaptivity and learning capabilities of the offline-trained NN and overcomes the computational challenges related to online NN training. In addition, for the proposed NN mechanism, training data are collected and stored in a highly random manner, which makes it much more feasible and efficient to implement lifetime adaptivity than any other methods. The proposed method is evaluated via both simulation and hardware experiments, which shows the great performance of the NN-based MTPA, MTPV, and flux-weakening control for an IPM motor over its full speed range. Overall, the proposed method can achieve a fast and accurate current reference generation with a simple NN structure, for optimal torque control of an IPM motor.

On Torque Vectoring Control: Review and Comparison of State-of-the-Art Approaches

Torque vectoring is a widely known technique to improve vehicle handling and to increase stability in limit conditions. With the advent of electric vehicles, this is becoming a key topic since it is possible to have distributed powertrains, i.e., multiple motors are adopted, in which each motor is controlled separately from the others. Moreover, electric motors deliver the torque required by the controller faster and more precisely than internal combustion engines, active differentials and conventional hydraulic brakes. The state of the art of Direct Yaw Moment Control (DYC) techniques, ranging from classical to modern control theories, are analyzed and discussed in this paper. The aim is to give an overview of the currently available approaches while identifying their drawbacks regarding performances and robustness when dealing with common issues like model uncertainties, external disturbances, friction limit and common state estimation problems. This contribution analyzes all the steps from the lateral dynamics reference generation to the desired control action computation and allocation to the available actuators. In addition, some of the presented control logic is evaluated in a simulation environment for a passenger car. Results of both open-loop and closed-loop maneuvers allow the comparison and clarification of each control strategy’s key advantages.

An interband cascade laser based heterodyne detector with integrated optical amplifier and local oscillator.

Heterodyne detection based on interband cascade lasers (ICL) has been demonstrated in a wide range of different applications. However, it is still often limited to bulky tabletop systems using individual components such as dual laser setups, beam shaping elements, and discrete detectors. In this work, a versatile integrated ICL platform is investigated for tackling this issue. A RF-optimized, two-section ICL approach is employed, consisting of a short section typically used for efficient modulation of the cavity field and a long gain section. Such a laser is operated in reversed mode, with the entire Fabry-Pérot waveguide utilized as a semiconductor optical amplifier (SOA) and the electrically separated short section as detector. Furthermore, a racetrack cavity is introduced as on-chip single-mode reference generator. The field of the racetrack cavity is coupled into the SOA waveguide via an 800 nm gap. By external injection of a single mode ICL operating at the appropriate wavelength, a heterodyne beating between the on-chip reference and the injected signal can be observed on the integrated detector section of the SOA-detector.