Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm

This paper presents distributed model predictive control methods for improving the transient response of feedback-controlled irrigation channels by managing the water-level reference set points across the irrigation season. The implemented distributed model predictive control methods exploit a two-level and single-level architectures for communication. For an automated irrigation channel, the satisfactory performance of the method with two-level architecture for communication in improving the transient response of automated irrigation channel is illustrated using computer simulation and compared with the performance of the distributed model predictive control method that exploits a single-level architecture for communication. It is illustrated that the distributed model predictive control method that exploits a two-level architecture for communication has a better performance in improving the transient response by better managing communication overhead.

In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

Wind plant control strategies, such as axial and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a $2\times 3$ array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed‐loop performance. In this case, it is desirable to design the pre‐existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre‐existing control system and the new control system are designed via Lyapunov‐based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov‐based controller that stabilizes the nominal closed‐loop system using only the pre‐existing control loops, two separate Lyapunov‐based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov‐based controller, improves the closed‐loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009

With an increasing number of vehicles on the road, multiple‐vehicle platoons have become a new solution under communication constraints. However, the existing studies seldom consider the effect of the tail vehicle on the information transfer and platoon control over long distances, and thus the control performance under time‐varying delays is unacceptable. In this study, a hierarchical multi‐platoon control architecture considering the backbone platoon and sub‐platoons is proposed under non‐ideal communication conditions, where the backbone platoon layer communication topology is designed to achieve backbone platoon control and appropriate coordination among sub‐platoons. Distributed model predictive controllers (DMPCs) for backbone‐ and sub‐platoons that integrate delay compensation have been proposed. Buffers and delay compensators in the proposed DMPC are designed to reduce the interference caused by non‐ideal communications. The asymptotic stability is analyzed at the sub‐ and multi‐platoon levels. The results show that compared with traditional DMPC without a compensator, the proposed DMPC can eliminate the velocity lag and steady‐state spacing errors and maintain the maximum velocity and spacing errors within 0.11 m/s and 0.17 m, respectively, under certain operating conditions. Thus, the proposed method guarantees the stability of multiple platoons under both the constant and time‐varying delay conditions.

With the high penetration of renewable energy, the active distribution network (ADN) and multi‐microgrids (MMGs), as emerging multi‐layered energy management systems, face challenges such as voltage violations and conflicts of interest among multiple agents. To address these distributed coordinating optimization issues in the ADN‐MMGs system, this paper proposes a coordinating optimization method for ADN‐MMGs systems that balances voltage robustness and system economics. In the day‐ahead stage, the method considers MMGs' participation in the voltage regulation ancillary service market and decentralized peer‐to‐peer (P2P) trading market, and baseline plans are developed for voltage stability and economic operation. In the intra‐day stage, ADN and MMGs are treated as subsystems. Using voltage sensitivity matrices and consensus algorithms, a state‐space‐based distributed model predictive control (Di‐MPC) coordination model is constructed, effectively mitigating short‐term power fluctuations from renewable energy and load variability. An analysis on a modified IEEE 33‐bus system demonstrates that the proposed method enhances economic efficiency and voltage robustness by coordinating subsystem energy flows. Compared to the distributed economic model predictive control (MPC) scheme, the proposed method reduces the state of charge (SOC) tracking error of MMGs by 51.21% and ensures that the deviation in planned daily operating cost for the ADN‐MMGs system does not exceed 1%.

Multi-UAVs tracking target in urban environment by model predictive control and Improved Grey Wolf Optimizer

This paper proposes a distributed model predictive control (DMPC) method with guaranteed stability for system with control coupled information and coupled constraints among the subsystems, which are described by piecewise affine (PWA) model. According to the condition of control input coefficient matrix of the PWA model, two frameworks are established. The DMPC optimal problem of each subsystem is converted into mixed integer quadratic programming (MIQP) to solve and obtain the control actions. To ensure the stability, equality constraint is applied.

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups.

In this paper, we propose a distributed model predictive control method with chance constraints for tracking reference signals. More specifically, considering a network of systems with independent dynamics, the proposed control scheme allows each subsystem to reach feasible setpoints, while respecting local and joint probabilistic constraints. Both a simulation example consisting of an academic temperature control problem and experimental results concerning the motion and coordination of moving agents are illustrated.

Vehicle platooning offers significant advantages in improving traffic efficiency and reducing energy consumption. While achieving stable tracking control is relatively straightforward on straight roads, ensuring platoon stability, energy efficiency, and computational efficiency on curved roads presents a substantial challenge due to the geometric constraints imposed by road curvature. To address this issue, this paper proposes a improved distributed model predictive control method designed for vehicle platooning on curved roads. The method combines model predictive control with Laguerre functions to enhance platooning stability on curved roads while alleviating the computational burden. To optimize energy efficiency, the vehicle’s energy consumption is integrated into the objective function. Additionally, deviations in control variables are penalized within the objective function to mitigate abrupt control actions. This, in turn, restricts rapid changes in vehicle acceleration, leading to improved driving comfort. Additionally, theoretical analysis confirms the asymptotic convergence of the proposed control system. Simulation results conducted under continuously varying speed and double-lane change scenarios demonstrate the effectiveness of the proposed method in tracking curved roads. Compared to the baseline control method, the proposed approach achieves superior tracking stability, improved energy efficiency, and reduced computational time. Notably, the proposed approach results in a 44.5% enhancement in tracking stability, a 26.8% improvement in energy efficiency, and a 9.8% reduction in computational cost under the PLF topology, highlighting its effectiveness on curved roads.

Radar-based road-traffic monitoring in urban environments

Radar systems play a crucial role in target tracking within urban environments, where challenges such as clutter, multipath effects, and electromagnetic interference significantly impact detection accuracy. Traditional radar methods often struggle to adapt to dynamic urban conditions, leading to decreased reliability in real-time target tracking. This study aims to develop and evaluate an AI-driven adaptive radar system that enhances tracking accuracy in urban settings. The research employs a quantitative approach using simulations to model radar signal processing under various environmental conditions. The AI model, based on Convolutional Neural Networks (CNN), is trained to optimize radar performance by filtering out noise and dynamically adjusting detection parameters. The results indicate that the AI-based radar system achieves a tracking accuracy of 95.2%, significantly outperforming traditional radar systems, which only reach 80% accuracy. Additionally, the AI-enhanced radar reduces response time to 120 milliseconds, compared to 250 milliseconds in conventional systems, demonstrating improved real-time processing capabilities. The system also exhibits greater resilience to high-clutter environments, maintaining stable target detection despite signal interference. These findings highlight the potential of AI in enhancing radar functionality for applications such as surveillance, traffic monitoring, and security. Future research should focus on integrating AI-driven radar with real-world radar hardware, exploring multi-sensor fusion, and refining adaptive learning techniques to further optimize tracking performance in complex environments

A distributed approach is proposed for planning a cooperative tracking task for a team of unmanned aerial vehicles (UAVs). In the scenario of interest UAVs are required to autonomously track, using their onboard sensors, a moving target in a known urban environment. The solution methodology involves finding visibility regions, from which a UAV can maintain a line of sight to the target during the scenario; and restricted regions, in which a UAV can not fly, due to the presence of buildings or other airspace limitations. A co-evolution genetic algorithm is derived for searching, in realtime, monotonically improving solutions. In the proposed distributed search method every UAV iteratively manipulates its own population of chromosomes, each encoding its control inputs in the calculated horizon. Team performance is attained by assigning fitness to each solution in the population based on the cooperative performance when using it together with preceding iteration tracking information obtained from teammates. Important attributes of the proposed solution approach are its scalability and robustness; and consequently it can be applied to large sized problems and adapt to the loss of UAV team members. The distributed nature of the algorithm also reduces the computation and communication loads. The performance of the algorithm is studied using a high fidelity simulation test-bed incorporating a visual database of the city of Tel-Aviv, Israel.

Urban city environments face the challenge of disturbances, which can create inconveniences for its citizens. These require timely detection and resolution, and more importantly timely preparedness on the part of city officials. We term these disturbances as anomalies, and pose the problem statement: if it is possible to also predict these anomalous events (proactive), and not just detect (reactive). While significant effort has been made in detecting anomalies in existing urban data, the prediction of future urban anomalies is much less well studied and understood. In this work, we formalize the future anomaly prediction problem in urban environments, such that those can be addressed in a more efficient and effective manner. We develop the Urban Anomaly PreDiction (UAPD) framework, which addresses a number of challenges, including the dynamic, spatial varieties of different categories of anomalies. Given the urban anomaly data to date, UAPD first detects the change point of each type of anomalies in the temporal dimension and then uses a tensor decomposition model to decouple the interrelations between the spatial and categorical dimensions. Finally, UAPD applies an autoregression method to predict which categories of anomalies will happen at each region in the future. We conduct extensive experiments in two urban environments, namely New York City and Pittsburgh. Experimental results demonstrate that UAPD outperforms alternative baselines across various settings, including different region and time-frame scales, as well as diverse categories of anomalies. Code related to this chapter is available at: https://bitbucket.org/xianwu9/uapd.