Cooperative Adaptive Research Articles

Data‐Driven Adaptive Cooperative Output Regulation for Completely Unknown Linear Multi‐Agent Systems Based on Finite Length Data

ABSTRACTWith respect to complex control systems, traditional model‐dependent methods are increasingly challenged, particularly when system models are unknown or intractable. Moreover, most past research has focused on systems that are partially or fully known. In this technical paper, a data‐driven paradigm is employed to investigate the cooperative output regulation problem (CORP) for completely unknown linear heterogeneous discrete multi‐agent systems (MASs). Input and state information are utilized to design effective control strategies and a novel data‐based algorithm is proposed with finite length data. An adaptive observer is designed to estimate the exosystem state, with only the leader's children having access to the unknown leader's system matrix. To address the challenge of unknown dynamics, the CORP is transformed into a linear quadratic regulation (LQR) problem by solving the regulation equation. Compared with the reinforcement learning method, the closed‐form optimal control gain is obtained directly from the relevant data without the need for an initial stabilization controller or iterative calculation. Simulation results validate the proposed scheme's effectiveness.

Fuzzy Adaptive Cooperative Prescribed Time Control for Nonlinear Vehicular Platoon Systems

Fuzzy Adaptive Cooperative Prescribed Time Control for Nonlinear Vehicular Platoon Systems

The potency of the adaptive cooperative learning model in strengthening numeracy performance of secondary school students: Education policy insights

The students' numeracy skills in Indonesia still need improvement. In order to strengthen numeracy skills for students, it is necessary to develop learning strategies that provide a fun and engaging learning environment for students by life involving the roles of parents, peers, and the community. This study aims to evaluate the effectiveness of the Adaptive Cooperative Learning model, developed by the researchers. Using a quasi-experimental design, the research involved a Cohort of around 32 students who participated in the product testing phase. The study incorporated a pretest, an experimental period, and a post-test. The findings revealed a marked improvement in student performance following the implementation of the Adaptive Cooperative Learning model, with the average score increasing to 79.44 and a standard deviation of 9.9. This equates to an N-gain of 0.77, which falls within the high range, suggesting that the model is effective in enhancing numeracy performance. The study concludes with several policy insights aimed at shaping future educational reforms in Indonesia, specifically in the area of numeracy teaching.

Cold-Adapted Fungi: Goldmine of Biomolecules Applicable in Industry

Fungi, which are widely distributed across the Earth, have successfully managed to colonize cold environments (e.g., polar regions, alpine ecosystems, and glaciers) despite the challenging conditions for life. They are capable of living in extremely harsh environments due to their ecological versatility and morphological plasticity. It is also believed that lower eukaryotes are the most adapted to life at low temperatures among microorganisms that thrive in cold environments. They play important ecological roles, contributing to nutrient recycling and organic matter mineralization. These highly specialized microorganisms have developed adaptation strategies to overcome the direct and indirect harmful influences of low temperatures. They have evolved a wide range of complex and cooperative adaptations at various cellular levels, including modifications to the cell envelope and enzymes, the production of cryoprotectants and chaperones, and the development of new metabolic functions. Adaptation to cold environments has made fungi an exciting source for the discovery of new cold-adapted enzymes (e.g., proteinases, lipases) and secondary metabolites (e.g., pigments, osmolytes, polyunsaturated fatty acids) for widespread use in biotechnology, food technology, agriculture, pharmaceutics, molecular biology, textile industry, and environmental bioremediation in cold climates. This review aims to provide a comprehensive overview of the adaptive strategies employed by psychrophilic yeasts and fungi, highlighting their ecological roles and biotechnological potential. Understanding these adaptive mechanisms not only sheds light on microbial life in extreme environments but also paves the way for innovative applications in the food industry and agriculture.

Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NOx emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

Psychological adaptations for fitness interdependence underlie cooperation across human ecologies.

Humans evolved to solve adaptive problems with kin and nonkin across fitness-relevant domains, including childcare and resource sharing, among others. Therefore, there is a great diversity in the types of interdependences humans experience across activities, relationships, and ecologies. To identify human psychological adaptations for cooperation, we argue that researchers must accurately characterize human fitness interdependence (FI). We propose a theoretical framework for assessing variation in FI that applies to the social interactions humans would have experienced across situations, relationships, and ecologies in the ancestral past and continue to experience today. According to this model, FI is characterized along four dimensions: (a) corresponding versus conflicting interests (b) mutual dependence versus independence, (c) asymmetrical versus symmetrical dependence (i.e., power), and (d) coordination. Because humans evolved to be highly mutually dependent on others to solve myriad adaptive problems, even compared to our closest living relatives, there is immense variability in the types of interdependences humans experience in daily life. Here, we describe the kinds of variation in interdependence humans experience, paying particular attention to social life in small-scale societies. In demonstrating the diversity of conflicts and coordination problems humans manage, we contend that humans evolved psychological adaptations to infer from signals, cues, and properties of the environment the four dimensions of FI under degrees of uncertainty to reduce the costs of cooperation. We conclude by discussing the theoretical implications of FI theory and emphasize that when individuals understand that others depend on them, it gives way to a new means of leverage to influence how others behave toward them. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Safety-aware human-lead vehicle platooning by proactively reacting to uncertain human behaving

Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the reliability of perception and decision-making. However, state-of-the-art HL-CACC technology still has a great limitation on driving safety due to the lack of considering the leading human driver’s uncertain behavior. In this study, a HL-CACC controller is designed based on Stochastic Model Predictive Control (SMPC). It is enabled to predict the driving intention of the leading Connected Human-Driven Vehicle (CHV). The proposed controller has the following features: (i) enhanced perceived safety in oscillating traffic; (ii) guaranteed safety against hard brakes; (iii) computational efficiency for real-time implementation. The proposed controller is evaluated on a PreScan&Simulink simulation platform. Real vehicle trajectory data is collected for the calibration of the simulation. Results reveal that the proposed controller: (i) improves perceived safety by 19.17 % in oscillating traffic; (ii) enhances actual safety by 7.76 % against hard brakes; (iii) is confirmed with string stability. The computation time is approximately 3.2 ms when running on a laptop equipped with an Intel i5-13500H CPU. This indicates the proposed controller is ready for real-time implementation.

Stability analysis and controller design of the Cooperative Adaptive Cruise Control platoon considering a rate-free time-varying communication delay and uncertainties

In recent years, Cooperative Adaptive Cruise Controls (CACCs) have been increasingly studied as a promising solution to problems such as traffic congestion and pollutant emissions. Despite their potential, the communication delays within CACC systems undermine the effectiveness of regular feedback control method in guaranteeing the fundamental control objective of stability. Considerable research has been conducted to derive stability conditions that account for communication delays. However, the time-varying and rate-free attributes of communication delay make deriving stability conditions highly challenging. To address this, this paper proposes a novel stability condition for the CACC platoon considering a rate-free communication delay using the Lyapunov–Krasovskii Stability Theorem and Schur complement. Additionally, we deduce a robust stability condition that takes into account measure uncertainties. Building on these foundations, a centralized H∞ controller is developed to address rate-free disturbances, ensuring string stability. Furthermore, extensive numerical analyses are conducted to investigate the impact of a rate-free communication delay and measurement uncertainties on tracking performance, transient response, and safety conditions. The results demonstrate that CACCs can effectively track errors and achieve equilibrium if the stability condition is met. Realistic scenarios incorporating rate-free communication delays and measurement uncertainties are associated with diminished tracking performance, transient responses, and safety conditions when compared to ideal scenarios characterized by constant communication delays. Furthermore, the H∞ controller surpasses the regular controller in tracking performance and maintains string stability amidst rate-free communication delays. Specifically, under the H∞ controller, the peak spacing error is reduced to merely 83.68% of that observed with the regular controller. The deployment of the H∞ controller facilitates a significant reduction in settling time (ST) by 90.04% and effectively prevents overshoot, thereby ensuring string stability, in stark contrast to the regular controller, which only achieves a 79.34% reduction in ST and a 5.97% reduction in maximum overshoot. Moreover, the H∞ controller markedly reduces the likelihood of high-risk scenarios in comparison to the regular controller. Moreover, CACCs with access to more distant and abundant information demonstrate superior transient response and safety conditions.

A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints

Artificial island construction is a multifaceted engineering endeavor that demands precise scheduling to optimize resource allocation, control costs, ensure safety, and minimize environmental impact within dynamic marine environments. This study introduces a comprehensive multi-objective optimization model that integrates critical factors such as resource limitations, task dependencies, environmental variability, safety risks, and regulatory compliance. To effectively address the complexities of this model, we develop and employ the Multi-Objective Adaptive Cooperative Evolutionary Marine Genetic Algorithm (MACEMGA). MACEMGA combines cooperative coevolution, adaptive dynamic weighting, dynamic penalty functions, and advanced genetic operators to navigate the solution space efficiently and identify Pareto optimal schedules. Through extensive computational experiments using data from the Dalian Bay Cross-Sea Traffic Engineering project, MACEMGA is benchmarked against algorithms such as NSGA-II, SPEA2, and MOEA/D. The results demonstrate that MACEMGA achieves a reduction in construction time from 32.8 to 23.5 months and cost savings from CNY 4105.3 million to CNY 3650.0 million while maintaining high-quality outcomes and compliance with environmental standards. Additionally, MACEMGA shows improvements in hypervolume by up to 15% over existing methods and a Convergence Rate that is 8% faster than MOEA/D.

ReCAP: Protecting Cooperative Adaptive Cruise Control Against Multi-Channel Perception Adversary

ReCAP: Protecting Cooperative Adaptive Cruise Control Against Multi-Channel Perception Adversary

A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article, lane change models for mixed traffic flow under cooperative adaptive cruise control (CACC) platoon formation are established. The analysis begins by examining the impact of lane changes on traffic flow stability. The influences of various factors such as lane change locations, timing, and the current traffic state on stability are discussed. In this analysis, it is assumed that the lane change location and the entry position in the adjacent lane have already been selected, without considering the specific intention behind the lane change. The speeds of the involved vehicles are adjusted based on an existing lane change model, and various conditions are analyzed for traffic flow disturbances, including duration, shock amplitude, and driving delays. Numerical calculations are provided to illustrate these effects. Additionally, traffic flow stability is factored into the lane change decision-making process. By incorporating disturbances to the fleet into the lane change income model, both a lane change intention model and a lane change execution model are constructed. These models are then compared with a model that does not account for stability, leading to the corresponding conclusions.

A cooperative lane change control strategy for cooperative adaptive cruise control platoons with insufficient headway gaps

Cooperative Adaptive Cruise Control (CACC) platoons contribute to enhancing road traffic safety and efficiency. However, creating suitable headway gaps for CACC platoons to change lanes is a challenge. This paper proposes a cooperative lane change control strategy for CACC platoons in mixed traffic environments where suitable headway gaps on the target lane are insufficient. Firstly, a cooperative longitudinal and lateral control strategy for CACC platoons is designed. Once longitudinal control ensures that the headway gap on the target lane meets the criteria, the vehicles are controlled to complete the lane change and resume cruising in their original formation. To achieve the longitudinal control objectives of gap generation by cooperative deceleration before lane change and cruising after lane change, a longitudinal cooperative control strategy that can independently create the lane change gap is proposed based on an adaptive weight model predictive control. Finally, the lane change headway gaps in the proposed strategy are generated sequentially for each vehicle, a contrast experiment is designed where the lane change headway gap is generated once and vehicles change lanes simultaneously. The effectiveness of the proposed strategy is validated through numerical simulation combining CarSim and Matlab/Simulink. The results show that the CACC platoon completed the lane change successfully under the control of the proposed strategy. Compared to the contrast simulation, in both low-speed and high-speed highway scenarios, the average speed standard deviation under the proposed strategy is reduced by 48.3 % and 39.9 % respectively, while the average comfort index decreases by 11.7 % and 5.9 %.

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.

Cooperative Adaptive Cruise Control Platoon Fleet Formation Model Considering Efficiency and Stability

In the existing formation model, vehicles in the same lane or adjacent lane are regarded as the structure, and the driving behavior of vehicles is studied from the perspectives of safety, speed consistency, and stability, and the speed control model is proposed from the perspective of vehicles themselves, to obtain a stable fleet with the same distance and speed. However, in this process, the initial condition of the vehicle, the traffic flow environment, and the efficiency of the fleet formation are less considered. Therefore, based on summarizing the existing fleet building model, this paper puts forward the rapid construction model and algorithm of a cooperative adaptive cruise control platoon fleet. One of the important goals of forming a team is to enter the team with the smoothest trajectory in the shortest time. Therefore, this chapter studies the trajectory optimization of the vehicle formation process from the perspective of vehicle dynamics.

Towards a livelihood-interdependence approach to framing adaptation in research and practice: evidence from farmers’ and herders’ relations in Northern Ghana

ABSTRACT Changing farmer–herder relations (FHR) are central to the social dynamics of climate change adaptation in African drylands, involving conflict and cooperation. While substantial literature has built a case for increased conflict of various kinds in West African drylands due to climate change stressors, this literature does not yet fully reflect the growing importance of more cooperative and interdependent elements of FHR. This study addresses this research gap through a case study of Gushiegu and Karaga Districts in Northern Ghana. Our analysis found growing cooperation between farmers and herders driven by livelihood diversification, sociocultural conditions such as increasing intermarriage, religious affiliation and the emergence of hybrid conflict resolution mechanisms that reflect a process of institutional bricolage. This deepening interdependence manifests in multiple ways, including knowledge exchange, management of common property resources and the strengthening of “bridging” forms of social capital. Our findings suggest a need to reframe adaptation in research, policy and practice to emphasize these beneficial forms of interdependence in FHR.

Distributed Adaptive Cooperative Tracking Control for UAV Swarm Formation With Dead-Zone Input

Distributed Adaptive Cooperative Tracking Control for UAV Swarm Formation With Dead-Zone Input

Multi-UAV Adaptive Cooperative Formation Trajectory Planning Based on an Improved MATD3 Algorithm of Deep Reinforcement Learning

Multi-UAV Adaptive Cooperative Formation Trajectory Planning Based on an Improved MATD3 Algorithm of Deep Reinforcement Learning

Comparative analysis of different spacing policies for longitudinal control in vehicle platooning

Most existing automated driving vehicles in the platoon equipped with an Adaptive Cruise Control (ACC) systems and a Cooperative Adaptive Cruise Control (CACC) systems have mainly focused on enhancing safety, improving traffic efficiency problems, and reducing workload. The spacing strategy is the core of all platoon designs, and the performance of an ACC/CACC systems hinges on the select of the spacing strategy. Although, in the literature, there are many papers dealing with platoon control, detailed explanations of the operating mechanisms of two types of spacing policies including the Headway Spacing Strategy (CTHS), and Constant Spacing Strategy (CSS), and comparative studies on them are still lacking. This work presents the studies of the longitudinal control strategy of a platoon of vehicles equipped with the existing ACC systems and CACC systems under two different spacing policies to evaluate the performances. The contributions in this work are carefully reviewed and the operating mechanisms and characteristics of two different spacing policies: the CTHS and CSS, the general evaluation criteria for spacing strategies are provided and their advantages and disadvantages are based on the numerical results. Both numerical simulations and experiments with a platoon of smart cars in real-time have demonstrated the effectiveness and practicability of the presented methodology.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.