Low-level Control Research Articles

A Strategy for Traffic Safety of Vehicular Platoons Under Connection Loss and Time-Delay

Autonomous platoons are good alternatives for cargo transportation, with many approaches to safety and efficiency issues. In the last decades, much effort has been employed in this context, with solutions ranging from low-level control laws to learning strategies. Although some papers have concentrated on methods robust to disturbances or resilient to disruptions, most of the current literature assumes that the platoon starts and remains connected all the time, even when subjecting vehicles to limited communication ranges and time-delay. Therefore, in this paper, we address the problem of connectivity maintenance and stability of platoons under the complete disconnection of vehicles. The main goal is to increase the tolerance to agent exits, external elements (such as traffic lights and human-driven vehicles), and non-ideal initial conditions, improving traffic safety in mixed traffic scenarios. By modeling the network connection as a Directed Acyclic Graph, we use a state-machine policy to recover connectivity and regulate the spacing distance to other vehicles under heterogeneous time-delays. We demonstrate that this state alteration can be interpreted as a reference tracking problem and propose a design procedure that provides stability and zero spacing error in steady-state. Our control protocol allows vehicles to reach a consensus with the team, even when they start disconnected from other ones. Results with agent-based and nonlinear simulations illustrate the effectiveness of our approach.

CRUSE - What the first 100 days have taught us.

Introduction: Health apps play an increasing role in everyday healthcare, especially for chronic diseases. The Chronic Urticaria Self Evaluation (CRUSE) is a new mobile health app for chronic spontaneous urticaria (CSU) patients, which replaces disease tracking via paper and pen, thus making disease monitoring more convenient, increasing tracking compliance, and improving data quality and access. Methods: CRUSE enables patients to complete patient-reported outcome measures on their smartphone and send the results, along with current medication and pictures, to their treating physician via email. CRUSE captures the urticaria (UAS) and angioedema activity (AAS) scores and the urticaria and angioedema control tests (UCT and AECT). In this work, a descriptive analysis of CRUSE users and reported days was performed. The global network of Urticaria Centers of Reference and Excellence (UCARE) provides the app and its data. Results: CRUSE is now available in Germany, Switzerland, Austria, the UK, Italy, Spain, France, and Turkey. Of 620 newly registered users (from July 1st until November 18th of 2022), 72 % were female, and the mean age was 36.6 years (17 - 78 years). The average daily UAS and AAS value (mean ± standard deviation) were 2.1 ± 1.9 and 7.2 ± 3.3, respectively. Most CRUSE patients had poorly controlled disease, with mean UCT values of 7.0 ± 4.4 and mean AECT values of 8.1 ± 4.5. Conclusion: The first days of patients with CSU using CRUSE confirm the high need for an app that helps to monitor disease activity, impact, and control. The first results indicate low levels of disease control in most CRUSE users, with low UCT and AECT values. Future analyses will assess follow-up documentation data and evaluate the effects of treatment changes on CSU activity, impact, and control.

The neuromechanics of animal locomotion: From biology to robotics and back.

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.

Defending State-Feedback Based Controllers Against Sensor Attacks

Context: This paper is motivated by the need to improve the resilience of industrial control systems. Many control systems currently operating in the industry were designed and implemented before the boom in communications (wired and wireless networks) within industrial control systems. However, nowadays they operate connected to the communications network. This increase in connectivity has made the systems susceptible to cyber-attacks that seek to degrade the proper operation of the control loop even affecting only one sensor.
 Method: We use concepts from fault tolerant control and classic control theory to show that it is possible to reconstruct the system state without (any) one of the system outputs. This fact is used in the recalculation of the control signal through an algorithm of attack detection and isolation, to avoid for an attack to be feedback to the system, mitigating its effect. We show the effectiveness of our proposal with simulations on a four-tanks testbed using Matlab and Simulink.
 Results: We show that a bank of unknown input observers can be designed to recover true information from attacked sensors, i.e., recover the information without the effect of the attack. Therefore, the estimation obtained from those observers can be utilized for computing a control action that mitigates the effect of the attack.
 Conclusions: This mitigation of the attack prevents a single sensor attack from signi cantly impair the action of low-level controllers, improving the resilience on the system just modifying the digital controller architecture. The development presented is limited to cyber-attacks on system sensors pre- sented one at a time, that still can compromise seriously the system behavior. Future work will address the extension of the presented results in situations with simultaneous attacks on more than one sensor, and/or consider attacks on the control system actuators.

Online PID Tuning Strategy for Hydraulic Servo Control Systems via SAC-Based Deep Reinforcement Learning

Proportional–integral–derivative (PID) control is the most common control technique used in hydraulic servo control systems. However, the nonlinearity and uncertainty of the hydraulic system make it challenging for PID control to achieve high-precision control. This paper proposes a novel control strategy that combines the soft actor-critic (SAC) reinforcement learning algorithm with the PID method to address this issue. The proposed control strategy consists of an upper-level controller based on the SAC algorithm and a lower-level controller based on the PID control method. The upper-level controller continuously tunes the control parameters of the lower-level controller based on the tracking error and system status. The lower-level controller performs real-time control for the hydraulic servo system with a control frequency 10 times higher than the upper controllers. Simulation experiments demonstrate that the proposed SAC-PID control strategy can effectively address disturbances and achieve high precision control for hydraulic servo control systems in uncertain working conditions compared with PID and fuzzy PID control methods. Therefore, the proposed control strategy offers a promising approach to improving the tracking performance of hydraulic servo systems.

Design of supervisory controllers to reduce energy consumption in plant-wide biological wastewater treatment plants with feedback signals from non-ideal sensors

In this research, proportional-integral (PI) and Fuzzy logic control (FLC) strategies are designed to control wastewater treatment processes at the whole plant level. Two control combination strategies are implemented to minimize the operational cost index (OCI) and effluent quality index (EQI). The benchmark simulation model (BSM2-P) is used as a working platform. In close resemblance to practical operations, the sensors are considered as non-ideal with delay and also having measurement noise. Binary-level control topology is designed in which the lower-level PI controller is used to control the dissolved oxygen (DO) levels in the second aerobic reactor by manipulating the oxygen mass transfer coefficient (KLa) of the last three aerobic reactors and FLC is designed at the supervisory level based on Ammonia levels. Ammonia in the second aerobic reactor is taken as a feedback signal for FLC to compute the DO setpoints. In comparison with non-supervisory control structures, operational cost is improved in PI-Fuzzy by 6.5%. Compared to the open-loop control, the PI controller improves effluent quality by 2.1%. Nevertheless, the combination of PI and fuzzy control shows high greenhouse gas production rates.

A review of the best practice in traffic calming evaluation

Traffic calming (TC) has been applied widely for several decades, although approaches to evaluating its effects on speeds have been inconsistent. This resulted in limited comparable and robust evidence to support practitioner guidance for TC design. To fill this gap and suggest best practices for the evaluation of TC effects on speeds, we performed a systematic review of 158 publications. We distilled information related to five research questions: Which measurement sensor was used? How was speed measured? Was free-flow speed considered? What was the sampling density? How were sample considerations reported? In addition to coding the studies based on these research questions, we rated them based on scientific robustness. The review confirmed the inconsistent state of evaluation practice. Most common evaluation approaches employed static detectors, low levels of control for bias (simple before-after), and unknown survey periods. The review found that the most robust evaluation practices involved multivariate before-after or cross-sectional study designs, used dynamic measurement of speeds (e.g., probe vehicles, simulations), large samples of vehicles, and more precise evaluation of speed changes using speed-distance graphs. These findings could guide more consistent and robust evaluation practice, and thus help to improve evidence-based TC guidance for creating safer and more sustainable neighborhoods.

Context-Dependent Shifts in Self-Regulatory Personality Processes During COVID-19: Changes in Control Predict Dynamic Shifts in Goal Reengagement Capacity.

The ability to reengage with new attainable goals after major setbacks is a core self-regulatory trait linked to health and well-being. Yet little is known about the extent to which such goal reengagement capacities may shift over time in response to changing contextual circumstances. Using a nationally-representative sample of U.S. adults aged 18-80 (n = 293), the present 1-year study examined whether changes in opportunity to influence life circumstances (perceived control) were coupled with dynamic shifts in goal reengagement capacity for individuals who differed in their levels of control at pandemic onset. Results of multilevel models showed that within-person increases in perceived control during the pandemic predicted corresponding within-person increases in goal reengagement capacity. Moderation models showed that the positive within-person association between perceived control and goal reengagement was pronounced for individuals with lower levels of control at pandemic onset who may be particularly sensitive to periods of opportunity to pursue new attainable goals. Findings inform theories of personality and self-regulation in pointing to contextual circumstances under which goal reengagement capacity exhibits dynamic shifts in populations who differ in their perceived opportunities for control.

Multilane freeway merging control via trajectory optimization in a mixed traffic environment

AbstractMerging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.

Bringing trust and transparency to the opaque world of waste management with blockchain: A Polkadot parathread application

The majority of countries are currently struggling with unsustainable levels of waste production and low levels of recycling, particularly relating to household waste, and this area is in urgent need of new solutions. In general, the waste management sector has struggled with low consumer trust, fraud, manipulation, significant manual processes, and low levels of information and control. Recent events relating to the COVID-19 pandemic have highlighted, in particular, the role of trust in effective public policy making and consumer behavioural change. Here we propose a hybrid blockchain solution called a Polkadot parachain. Polkadot is a blockchain technology that connects a network of blockchains, each called a parachain, that can be customised to the business needs of a given application. This solution provides the cost benefits, scalability, and control of a permissioned or private blockchain while providing the security, verifiability, and trust of a public blockchain. The solution is developed with a design science approach and combines three typically separate blockchain use cases: supply chain tracking, incentivisation through a payment system, and gamification to achieve a complete solution for waste management. We provide a detailed discussion on the design of this blockchain solution with the use of blockchain functionality assessed against the criteria and development approaches found in the literature. Finally, we demonstrate how such a blockchain can be implemented with the Substrate blockchain development framework.

Transport Infrastructure Investments as a Factor of Economic Growth of European Union Countries

Abstract Territorial cohesion and improved accessibility are among the main objectives of the European Union (EU) Cohesion policy. Although there is a wealth of literature on the impact of investment in transport infrastructure on overall accessibility levels, only a few studies have tried to evaluate the possible link between transport infrastructure investment (TII) and economic growth in the EU. Our research aims to evaluate the impact of TII on economic growth in the EU. The methodology is based on nonlinear neoclassical specification, which includes a multiplicative term to evaluate if the absence of corruption mediates the effect of TII on growth. Our unbalanced panel data covers 27 EU countries (Cyprus is excluded due to missing data and the United Kingdom is a part of the sample) from 2000 through 2019. Our research confirms a positive and statistically significant impact of TII on economic growth but with a diminishing return. Estimating the absence of corruption as a possible moderator of the effect that TII has on growth shows that control of corruption plays a significant role in the TII growth nexus. We find that in countries with a low level of control of corruption, TII has a statistically significant negative effect on growth.

Neurorehabilitation robotics: how much control should therapists have?

Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.

Fault tolerant control system for an ROV based on a novel integral sliding mode control and a state and fault observer in the presence of thruster limitations

This paper presents a novel fault tolerant control system for remotely operated vehicles in the presence of unknown system dynamics and unknown thruster faults and failures while accounting for magnitude and rate limits of the thrusters. The proposed control system consists of a novel integral sliding mode control (ISMC) and a new state and fault observer. The proposed ISMC utilizes a novel asymptotic sliding surface to enhance the performance of the control system during both initial transients and steady-state conditions. The observer takes thrusters system dynamics into account and provides estimation of faults and failures. Control allocation is utilized for fault accommodation and to separate the high-level control system and the low-level thruster controllers. Stability analyses are carried out and it is shown that the state observer is guaranteed to be fixed-time convergent while the fault observation error is input-to-state stable. It is also shown that the ISMC-based closed-loop control system is uniformly-ultimately bounded with practically fixed-time reaching phase. Simulations are carried out and comparisons are made with several fixed-time and finite-time control systems. The results outline the superior performance of the proposed control system in terms of positioning accuracy and fault tolerance during initial transients and steady-state conditions.

Proposed frequency decoupling-based fuzzy logic control for power allocation and state-of-charge recovery of hybrid energy storage systems adopting multi-level energy management for multi-DC-microgrids

This paper proposes a decentralized multiple-Direct-Current-Microgrid (multi-DCMG) system to supply affordable load demands while addressing challenges posed by Hybridized-Energy-Storage-Systems (H-ESS) limitations, consumption/generation complexities, and renewables volatility. The paper's contributions include a system feasibility assessment for isolated users and a demonstration of the effectiveness of the control strategies adopted. To improve system resiliency and reliability, the proposed system adopts a high-control level for energy/power balances, using a Mamdani 50 rule-based Fuzzy Logic energy management system (FL-EMS) to supervise State-of-Charge (SoC) recovery. The low-control level manages/supervises DC-DC power converters' powers adopting Proportional-Integral (PI), Hysteresis-Current-Controller (HCC), and Linear-Quadratic-Regulator (LQR) in closed-Control-loops, besides an advanced low-pass-filtering (A-LPF) for load frequency decoupling. The results show that the proposed H-ESS outperformed single-ESS systems in dynamic load changes and renewables' uncertainty, and supercapacitors improved load supply, voltage regulation, and current tracking. However, expensive costs and slow restoration of H-ESS banks from critical SoCs are major drawbacks. The global system assessment demonstrated promising results through proper FL-EMS setpoint computation, stable Bus voltage with 0.55–6.9% deviations due to robust controllers, accurate SoC recovery of HESS batteries at critical SoCs (<10% and >90%), fast and accurate convergence with 3.35–3.37% mismatch, and 99.3% supply efficiency at minor power losses of 0.7–1.55%.

Polysubstance use and its correlation with psychosocial and health risk behaviours among more than 95,000 Norwegian adolescents during the COVID-19 pandemic (January to May 2021): a latent profile analysis.

Polysubstance use represents an adolescent health risk; however, large-scale studies investigating this issue during the COVID-19 pandemic are scarce. We aim to (i) characterise substance use profiles among adolescents and (ii) identify correlates of such substance use profiles. Norwegian nationwide survey data from 2021 were analysed using latent profile analysis. Participants were 97,429 adolescents aged 13-18. We assessed cigarette, e-cigarette and snus use, alcohol consumption, and cannabis and other illicit drug use. Correlates included psychosocial variables, health risk behaviours, and COVID-19-related problems. We identified three adolescent profiles; those who use no substances (n=88,890; 91%); those who use snus and alcohol (n=6546; 7%); and those who use multiple substances (i.e., polysubstance profile; n=1993; 2%). Boys, older adolescents, adolescents with lower socio-economic status, and those reporting low levels of parental control, and higher parental alcohol use, mental health problems, pain-related variables, and other health risk behaviours were most likely to be in the polysubstance profile. Adolescents with social and mental health issues related to COVID-19 were more at risk of being in the polysubstance profile. Adolescents who use snus and alcohol showed similar patterns of risk factors, but on a somewhat lower level than those in the polysubstance profile. Adolescents who use multiple substances have an unhealthier lifestyle, are at a higher risk of experiencing psychosocial impairments, and report more problems related to the COVID-19 pandemic. Preventative strategies to reduce polysubstance use might help promote psychosocial well-being in adolescents across various life domains. This study was funded by two grants from the Research Council of Norway (project #: 288083 and 300816). The Norwegian Directorate of Health has funded the data collection. The Research Council of Norway and the Norwegian Directorate of Health have not had any role in study design, data collection, data analysis, interpretation, and writing of the report.

Intention Prediction-Based Control for Vehicle Platoon to Handle Driver Cut-In

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.

Optimization of Automatic Control and Measurement for the HEPS Large-Scale High-Power RF Conditioning Test

High power radio frequency (RF) conditioning is a key step for the RF components to gain full performance. In the construction of high energy photon source (HEPS), upgrading Beijing Electron-Positron Collider (BEPCII), preresearch of circular electron positron collider (CEPC), and other projects at Institute of High Energy Physics (IHEP), there will be a lot of RF conditioning tasks within a very tight schedule. To improve the conditioning efficiency, an optimized automatic control has been developed. The automatic control system is developed based on the Experimental Physics and Industrial Control System (EPICS). The control hardware platform adopts a combination of the Micro Telecom Computing Architecture (MTCA) based low-level control and programmable logic controller (PLC) interlocking unit, which integrates a variety of measurements to ensure safe and reliable unattended operations during high-power (up to 160 MW) and high gradient (up to 33 MV/m) testing. The finite state transfer based on multiple event-triggered interrupt processes is used to improve conditioning efficiency. Taking the conditioning of the HEPS 3-m-long acceleration structure as an example, the breakdown rate (BDR) at high power is less than one out of 180 000 pulses. The time consumed for each accelerating structure is reduced by a factor of nearly 2/3. Experiments show that the system realizes the unattended operation of whole process, and improves conditioning efficiency.

Impact of executive pay gap on environmental, social, and governance disclosure in China: Is there a strategic choice?

AbstractThis study investigates the mechanisms through which the executive pay gap affects the disclosure of environmental, social, and governance (ESG) information. Utilizing panel data from China's A‐share listed firms between 2012 and 2020, our findings reveal a significant positive correlation between the executive pay gap and ESG disclosure, with real earnings management playing a partial mediating role. Notably, the impact of the executive pay gap on ESG disclosure is more pronounced in voluntary disclosure firms and those with low internal control levels, as compared to mandatory disclosure firms and those with high internal control levels. This study provides international evidence for the strategic disclosure behavior of ESG information and the necessity of ESG disclosure regulation.

Analysis on the Control Strategies of Hip Exoskeletons and Exosuits

Lower limb exoskeleton has been proven to be effective in gait training and locomotion assistance. Specifically, control strategies of exoskeletons play the most important role in safe and effective interaction between the user, the exoskeleton, and the environment. In this paper, an analysis on the control strategies of hip exoskeletons and exosuits is performed. The controls are divided into three levels: high, medium, and low. The definition of high-level control as well as the corresponding control methods are listed and the principle of each is explained. Then, the mid-level control section is divided between detection and action layer and each control that belongs to one of the two categories is analyzed. The definition of low-level control and its control methods are then explained. At last, according to the results of various studies, the advantages and potential disadvantages of each method are put forward. The purpose of this paper is to provide reference and guidance for future researchers who hope to develop new controls or improve existing control strategies for hip exoskeletons and exosuits.

Integrated Path Tracking Controller of Underground Articulated Vehicle Based on Nonlinear Model Predictive Control

This paper proposes an integrated path tracking controller for articulated vehicles. A nonlinear model-predictive control (NMPC)-based reference state tracker is designed as an upper-level controller to solve the vehicle’s longitudinal velocity and steering rate. A terminal cost is introduced into the NMPC to improve the controller’s stability. A lower-level controller is developed to translate upper-level solutions into vehicle actuators’ signals, including steering and driving controllers. The steering controller translates the steering rate into the linear velocity of the cylinder to calculate the required fluid volume and ultimately into the rotation speed of the steering motor. The neural network method is applied in the driving controller to ensure accuracy under different loadings. In order to investigate the effects of the path tracking controller, an articulated dump truck is adapted for the field tests by adding the steering-by-wire system and driving-by-wire system, respectively. Experimental verifications of the lower-level controller are performed. The results show that the controller can accurately satisfy the demand. Finally, the tracking performance of the integrated path tracking controller is analyzed experimentally under different reference velocities. The results indicate that tracking accuracy can be guaranteed.