Control Architecture Research Articles

Integrated Controller for Fuel Cell Systems: A Full-loop Architecture

Abstract In response to the global initiative towards hydrogen energy, increasing focus has been placed on enhancing the performance, reliability and endurance of fuel cells by utilizing advanced control and monitoring strategies. However, due to the multi-variable. multi-loop and multi-physics nature of hydrogen fuel cells, the current decentralized architecture, where fuel cell controllers are isolated and placed in separate enclosures is no longer sufficient to carry out intricate coordinated control strategies. To this end, in this paper, we introduce a novel full-loop architecture, which enables the integration of the fuel cell controller, the air compressor controller, and the power electronics controller within one enclosure, reducing system size and cost. Moreover, based on the integrated hardware architecture, coordinated control such as oxygen/hydrogen pressure coordination can be carried out efficiently. A case study on electrochemical impedance spectroscopy has been conducted, demonstrating the advanced control and monitoring capabilities of this controller architecture.

Application of Lock-in Amplifier Technique in Signal Blind Source Separation

Abstract In the realm of signal processing, frequency error stands as a critical challenge that cannot be overlooked, particularly in scenarios characterized by noise disturbances and constantly changing environmental variables, as it significantly undermines the precision of waveform separation. Faced with a spectrum of frequency error phenomena stemming from hardware instability, such as clock drift, transmission distortion, and environmental changes, this paper proposes a waveform separation scheme centered around a core closed-loop control architecture aimed at addressing such challenges. Leveraging the high-performance STM32F407 microcontroller platform, the system harnesses the powerful algorithmic advantages of Fast Fourier Transform (FFT) to precisely locate and delineate the spectral characteristics of signals. Furthermore, we creatively integrate lock-in amplifier technology into the meticulously designed closed-loop control system, coupled with state-of-the-art zero-crossing detection algorithms for in-depth optimization. The research findings demonstrate outstanding signal-to-noise ratio performance within predefined target frequency bands for the separation device based on lock-in amplifier technology, showcasing not only remarkable resilience against interference but also the ability to accurately separate and reconstruct target signal components with high fidelity from complex and dynamic signal environments. This significantly enhances the robustness and accuracy of the entire system.

Integrating optical security management with optical‐layer controller architecture for enhanced network security

AbstractTo guarantee the availability, confidentiality, and integrity of data transferred over optical channels—especially in the context of fifth‐generation (5G) communication infrastructure—optical network security management is essential. This paper provides an overview of security management for optical networks, emphasizing the importance of this practice in today's communication infrastructure and the difficulties presented by ever changing cyberthreats. The architecture of optical security management is shown, with special attention to how well it integrates with current optical‐layer controllers and how it facilitates the coordination of security operations among optical networks. The study also looks at use cases for optical network security management in 5G networks, such as safe data transfer, defense against cyberattacks, maintaining privacy in 5G apps, network slicing security, and resistance to physical assaults. Such instances highlight the adaptability and significance of optical network security management in bolstering 5G networks' security, privacy, and resilience across a range of businesses and applications.

Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation

This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the pointwise value of the uncertainty, i.e., the discrepancy between a nominal model and the true dynamics, with pre-computable estimation error bounds, and a robust Riemannian energy condition for computing the control signal. Under certain conditions, the controller guarantees exponential trajectory convergence during the learning transients, while learning can improve robustness and facilitate better trajectory planning. Simulation results validate the efficacy of the proposed control architecture.

IOT Based Smart Grid to Remotely Monitor and Control Renewable Energy Sources using Cloud Computing

Abstract: The growing demand for clean and sustainable energy sources necessitates advancements in grid structure. This project explores the potential of an IOT-based smart grid to remotely monitor and control renewable energy sources. In this, we use solar energy as a renewable energy source, and with the help of cloud computing, we monitor and control different types of loads. The paper highlights the key components, functionalities, and benefits of such a system.The increasing demand for sustainable energy solutions fuels the development of smart grid technologies. This project proposes an IOT (Internet of Things)-enabled smart grid architecture for remote monitoring and control of renewable energy sources. The core concept involves integrating sensors with renewable energy sources like solar or wind turbines. These sensors collect real-time data on energy generation, environmental conditions, and system health. This data is then transmitted over a network to a central control system or a cloud platform for further analysis. By implementing remote control capabilities, grid operators can: regulate energy production based on demand; integrate storage solutions for surplus energy; and improve overall grid stability and reliability. The project concludes by outlining the benefits of this approach, including a reduced carbon footprint, cost savings, and a more sustainable energy future.

A Dynamic Controller Architecture for Wheeled Mobile Robot Trajectory Tracking Utilizing Feedback Linearization and State Feedback

A Dynamic Controller Architecture for Wheeled Mobile Robot Trajectory Tracking Utilizing Feedback Linearization and State Feedback

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

People use their hands for intricate tasks like playing musical instruments, employing myriad touch sensations to inform motor control. In contrast, current prosthetic hands lack comprehensive haptic feedback and exhibit rudimentary multitasking functionality. Limited research has explored the potential of upper limb amputees to feel, perceive, and respond to multiple channels of simultaneously activated haptic feedback to concurrently control the individual fingers of dexterous prosthetic hands. This study introduces a novel control architecture for three amputees and nine additional subjects to concurrently control individual fingers of an artificial hand using two channels of context-specific haptic feedback. Artificial neural networks (ANNs) recognize subjects’ electromyogram (EMG) patterns governing the artificial hand controller. ANNs also classify the directions objects slip across tactile sensors on the robotic fingertips, which are encoded via the vibration frequency of wearable vibrotactile actuators. Subjects implement control strategies with each finger simultaneously to prevent or permit slip as desired, achieving a 94.49% ± 8.79% overall success rate. Although no statistically significant difference exists between amputees’ and non-amputees’ success rates, amputees require more time to respond to simultaneous haptic feedback signals, suggesting a higher cognitive load. Nevertheless, amputees can accurately interpret multiple channels of nuanced haptic feedback to concurrently control individual robotic fingers, addressing the challenge of multitasking with dexterous prosthetic hands.

Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties

In control of aerospace systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions. This paper presents a robust adaptive control architecture for linear parameter-varying (LPV) systems that allows for the desired dynamics to be systematically scheduled, while being able to handle a broad class of uncertainties, both matched and unmatched, which can depend on both time and states. The proposed controller adopts an L1 adaptive control architecture for designing the adaptive control law and peak-to-peak gain (PPG) minimization for designing the robust control law to mitigate the effect of unmatched uncertainties. Leveraging the PPG bound of a LPV system, we derive transient and steady-state performance bounds in terms of the input and output signals of the actual closed-loop system with respect to a nominal system. The proposed control architecture is applied to control the longitudinal motion of an F-16 aircraft operating within a large envelope. Simulation results using both LPV and fully nonlinear models validate the efficacy of the proposed method.

Dynamic ancillary services: From grid codes to transfer function-based converter control

Conventional grid-code specifications for dynamic ancillary services provision such as fast frequency and voltage regulation are typically defined by means of piece-wise linear step-response capability curves in the time domain. However, although the specification of such time-domain curves is straightforward, their practical implementation in a converter-based generation system is not immediate, and no customary methods have been developed yet. In this paper, we thus propose a systematic approach for the practical implementation of piece-wise linear time-domain curves to provide dynamic ancillary services by converter-based generation systems, while ensuring grid-code and device-level requirements to be reliably satisfied. Namely, we translate the piece-wise linear time-domain curves for active and reactive power provision in response to a frequency and voltage step change into a desired rational parametric transfer function in the frequency domain, which defines a dynamic response behavior to be realized by the converter. The obtained transfer function can be easily implemented e.g. via a proportional-integral (PI)-based matching control in the power loop of standard converter control architectures. We demonstrate the performance of our method in numerical grid-code compliance tests, and reveal its superiority over classical droop and virtual inertia schemes which may not satisfy the grid codes due to their structural limitations.

Investigation of grid-forming and grid-following converter multi-machine interactions under different control architectures

The proliferation of converter-interfaced generation necessitates the investigation of novel small-signal multi-machine interactions. The flexibility and lack of standardisation of converter control approaches results in a plethora of potential implementations, bringing different dynamics that can interact with each other and existing elements of the power system. This paper performs a small-signal analysis of power systems with the inclusion of grid-forming and grid-following converters for varying combinations of common control architectures, in terms of cascaded control loops, within the literature. Investigations are performed for a two-machine system and the WSCC 9-bus (three-machine) system. As well as interaction identification and characterisation, the impact of varying transmission line lengths, system loading, and generation dispatch are investigated.

Parameter Estimation Method for Virtual Power Plant Frequency Response Model Based on SLP

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a “centralized/decentralized” hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid.

Cyber–physical collaborative control for DC microgrid clusters under joint cyber-attacks

DC microgrid (DCMG) clusters, as deeply integrated cyber–physical systems (CPSs), are vulnerable to cyber-attacks like false data-injection attacks (FDIAs) and denial-of-service (DoS) attacks. This article proposes a cyber–physical collaborative control method, against the joint attacks mentioned above. The main contribution of this work is as follows: firstly, a collaborative control architecture based on multiagent systems (MASs) is constructed for the CPS; secondly, in the physical layer, a hierarchical control framework for DCMG agents is adopted, including primary, secondary, and tertiary control layers. The secondary and tertiary control layers can adjust the scheme of power flow management and frequency/voltage control, through the interaction information between agents; thirdly, in the cyber layer, an active defense strategy based on layered event-triggered protocol has been proposed: (1) according to the tampering of transmission data/information caused by FDIA, a support vector machine (SVM) based prediction learning compensation method is employed, to ensure the effectiveness of energy management and voltage/frequency control. (2) according to the transmission path interruption caused by DoS attack, the optimal path selection method and path reconstruction method are proposed to maintain the transmission of power flow management information and control information. Simulation results are presented to illustrate the effectiveness of real-time collaborative control for the proposed control method.

A new robust controller based on type-1 fuzzy logic and IP regulator for induction motor

The novelty of the work proposed a new controller based on type 1 fuzzy logic and IP regulator, the new controller applies to the asynchronous machine driven by a PWM inverter. Flux-directed vector control is considered one of the most effective control methods due to its ease of design and implementation. Proportional integral (PI) controllers are used to implement this method. The controllers parameters are calculated using traditional analytical methods directly from the machine parameters. This requires rigorous calculation and a thorough understanding of all machine parameters. Improve the performance of flow-oriented vector control (reduction of oscillations, driving of loads at variable speeds, etc.). The performance improvement was achieved firstly by changing the location of the proportional and integral regulators. Then, by a regulator that combines the integral proportional (IP) regulator and type 1 fuzzy logic in the MATLAB environment. A new architecture of the flow-oriented vector control controller, based on the combination between the IP regulator and type 1 fuzzy logic. The control makes it possible to improve the dynamic performance of the induction motor. The main possibility of creating an efficient controller based on the fuzzy logic of type 1 of the induction motor.

Improved Drycooler control by custom hybrid controller

Refrigeration devices designed for use in industrial environments are typically equipped with universal control algorithms, which require a minimal number of signals and parameters to ensure satisfactory device operation. These algorithms are typically of the PID type. This study elaborates upon the impact of using a dedicated hybrid controller, which was designed with specific consideration of the operating conditions of a given refrigeration device. The identified nonlinear Drycooler characteristics were used to support the controller at steady-state operating conditions. The system dynamics were supervised by a dedicated, experience-based designed hybrid fuzzy logic Mamdani type controller for both freecooling and compressor modes. The switchable configuration of the MISO control architecture results in a reduction in overshoots and oscillations in the system, a decrease in the time necessary for stabilization, a reduction in CO2, and an increase in control quality and energy efficiency.

Adaptive Multi-Surface Sliding Mode Control with Radial Basis Function Neural Networks and Reinforcement Learning for Multirotor Slung Load Systems

While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems.

Aerodynamic Feedforward-Feedback Architecture for Tailsitter Control in Hybrid Flight Regimes

This paper presents a guidance-control design methodology for the autonomous maneuvering of tailsitter transitioning unmanned aerial systems (t-UASs) in hybrid flight regimes (i.e., the dynamics between VTOL and fixed-wing regimes). The tailsitter guidance-control architecture consists of a trajectory planner, an outer-loop position controller, an inner-loop attitude controller, and a control allocator. The trajectory planner uses a simplified tailsitter model, with aerodynamic and wake effect considerations, to generate a set of transition trajectories with associated aerodynamic force estimates based on an optimization metric specified by a human operator (minimum time transition). The outer-loop controller then uses the aerodynamic force estimate computed by the trajectory planner as a feedforward signal alongside feedback linearization of the outer-loop dynamics for 6DOF position control. The inner-loop attitude controller is a standard nonlinear dynamic inversion control law that generates the desired pitch, roll, and yaw moments, which are then converted to the appropriate rotor speeds by the control allocator. Analytical conditions for robust stability are derived for the outer-loop position controller to guarantee performance in the presence of uncertainty in the feedforward aerodynamic force compensation. Finally, both tracking performance and stability of the control architecture are evaluated on a high-fidelity flight dynamics simulation of a quadrotor biplane tailsitter for flight missions that demand high maneuverability in transition between flight modes.

Optimality principles in spacecraft neural guidance and control.

This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale.

Integrating dynamic economic optimization and encrypted control for cyber‐resilient operation of nonlinear processes

AbstractThis article proposes a two‐layer framework to maximize economic performance through dynamic process economics optimization while addressing fluctuating real‐world economics and enhancing cyberattack resilience via encryption in the feedback control layer for nonlinear processes. The upper layer employs a Lyapunov‐based economic model predictive control scheme, receiving updated economic information for each operating period, while the lower layer utilizes an encrypted linear feedback control system. Encrypted state information is decrypted in the upper layer to determine the economically optimal dynamic operating trajectory through nonlinear optimization. Conversely, the lower layer securely tracks this trajectory in an encrypted space without decryption. To mitigate the cyber vulnerability of the upper layer, we integrate a cyberattack detector that utilizes sensor‐derived data for attack detection. We quantify the errors stemming from quantization, disturbances, and sample‐and‐hold controller implementation. Simulation results of a nonlinear chemical process highlight the robustness and economic benefits of this new control architecture.

Decentralized, norm-free, and adaptive event-triggered distributed control of nonholonomic mobile robots

The overarching contribution of this paper is a novel event-triggered distributed control architecture for multiagent systems that are composed of a team of nonholonomic mobile robots. Specifically, the robot dynamics are first feedback linearised to equivalently represent each robot with double integrator dynamics and the event-triggering law is then presented. This law is characterised by being decentralised (depending only on own signals of a robot), norm-free (independent of distances for reducing robot-to-robot data transmissions), and adaptive (estimating signals without using global information). Moreover, its system-theo- retical analysis is provided, which holds for both the sampled data exchange method and the solution-predictor curve-based data exchange method. In contrast to the well-studied sampled data exchange, the solution-predictor curve method further reduces robot-to-robot data transmissions by making each agent to store a generic form of this curve and exchange its parameters when an event occurs for approximating the solution trajectory of each robot. Finally, an illustrative numerical example is included to demonstrate the overall architecture.

Lignin microcapsules prepared on the basis of flexible skeleton with high foliar retention and UV shielding properties

Lignin-based microcapsules are extremely attractive for their biodegradability and photolysis resistance. However, the water-soluble all-lignin shells were unsatisfactory in terms of rainfall and foliar retention, and lacked the test of agricultural production practices. Herein, a novel microcapsule based on a flexible skeleton formed by interfacial polymerization and absorbed with lignin particles (LPMCs) was prepared in this study. Further analysis demonstrated that the shell was formed by cross-linking the two materials in layers and showed excellent flexibility and photolysis resistance. The pesticide loaded LPMCs showed about 98.68 % and 73.00 % improvement in scour resistance and photolysis resistance, respectively, as compared to the bare active ingredient. The foliar retention performance of LPMCs was tested in peanut plantations during the rainy season. LPMCs loaded with pyraclostrobin (Pyr) and tebuconazole (Teb) exhibited the best foliar disease control and optimum plant architecture, resulting in an increase in yield of about 5.36 %. LPMCs have a promising application prospect in the efficient pesticide utilization, by controlling its deformation, adhesion and release, an effective strategy for controlling diseases and managing plant growth was developed.