Search Results (22)

The increasing penetration of distributed energy resources (DERs) such as solar photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) in low-voltage (LV) and medium-voltage (MV) distribution networks is reshaping traditional grid operations. This shift introduces challenges including voltage violations, thermal overloading, and power quality issues due to bidirectional power flows. Hosting capacity (HC) assessment has become essential for quantifying and optimizing DER integration while ensuring grid stability. This paper reviews state-of-the-art HC assessment methods, including deterministic, stochastic, time-series, and AI-based approaches. Techniques for enhancing HC—such as on-load tap changers, reactive power control, and network reconfiguration—are also discussed. A key focus is the emerging concept of dynamic operating envelopes (DOEs), which enable real-time allocation of HC by dynamically adjusting import/export limits for DERs based on operational conditions. The paper examines the benefits, challenges, and implementation of DOEs, supported by insights from Australian projects. Technical, regulatory, and social aspects are addressed, including network visibility, DER uncertainty, scalability, and cybersecurity. The study highlights the potential of integrating DOEs with other HC enhancement strategies to support efficient, reliable, and scalable DER integration in modern distribution networks. Full article

The hybrid unmanned aerial vehicle combines the vertical take-off and landing and hover abilities of rotary-wing UAVs with the high-speed cruise and long-endurance capabilities of fixed-wing UAVs, expanding the flight envelope and application areas. The designed controller must handle the highly nonlinear dynamics and variable actuators resulting from this combination. Furthermore, the performance of the controller is also influenced by uncertainties in model parameters and external disturbances. To address these issues, a unified robust disturbance rejection control based on fixed-time stability theory is proposed for attitude control. A fixed-time disturbance observer is utilized to estimate composite disturbances without some strict assumptions. Based on this observer, a nonsingular chattering-free fixed-time integral sliding mode control law is introduced to ensure that tracking errors converge to the origin within a fixed time. In addition, an optimized control allocator based on the weighted least squares method is designed to handle the overactuation of a dual-system hybrid UAV. Finally, numerical simulations and hardware-in-the-loop experiments under different flight modes and disturbance conditions are carried out, and compared with nonlinear dynamic inverse and the nonsingular terminal sliding mode control based on a finite-time observer, the developed controller enhances attitude angle tracking accuracy and disturbance rejection performance. Full article

Steer-by-wire technology, critical for autonomous driving, enables full-wheel steering in articulated vehicles, significantly enhancing maneuverability in complex driving environments. This study investigates dynamic envelope optimization for articulated multi-body vehicles by integrating coordinated multi-axle steering control strategies with higher-order Bezier curve designs. Unlike traditional approaches that primarily focus on single-axle steering, this research emphasizes the advantages of multi-axle steering control, which significantly reduces the dynamic envelope and enhances maneuverability. To address the challenges posed by constrained road environments, a comparative analysis of Septimic Bezier curves under various control point configurations was conducted, demonstrating their effectiveness in achieving smoother curvature transitions and steering comfort. The results highlight the pivotal role of reducing curvature peaks and increasing curvature continuity in optimizing vehicle performance. Furthermore, advanced steering control strategies, such as Articulation Angle Reference (AAR) and Dual Ackermann Steering (DAS), were shown to outperform conventional methods by ensuring precise trajectory control and improved stability. This study provides actionable insights for enhancing vehicle handling and safety in complex driving scenarios, offering a framework for future road design and multi-axle steering system development. Full article

Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation. Full article

The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even in challenging scenarios. This paper introduces a novel modular upstream control architecture for overactuated vehicles, integrating a fast and robust linear time-varying model predictive path and speed tracking controller with a model following approach and nonlinear control allocation to form a holistic vehicle motion controller. The architecture decouples the path and speed tracking task from the actuator allocation, where torque vectoring and rear-wheel steering are applied to achieve linear understeer reference vehicle behavior. It allows for the use of a simpler path tracking controller, enabling long preview horizons and enhanced computational efficiency. Nonlinearities, such as the mutual influence of lateral and longitudinal tyre forces, are accounted for within the control allocation. The simulation results demonstrate that the proposed control architecture and overactuation improve vehicle stability in critical driving conditions and reduce path tracking errors compared to a dual-motor vehicle. Full article

The aim of this study was to compare the correlation between electromyography (EMG) activity and vehicle motion during double lane change driving. This study measured five vehicle motions: the steering wheel angle, steering wheel torque, lateral acceleration, roll angle, and yaw velocity. The EMG activity for 19 muscles and vehicle motions was applied for envelope detection. There was a significantly high positive correlation between muscles (mean correlation coefficient) for sternocleidomastoid (0.62) and biceps brachii (0.71) and vehicle motions for steering wheel angle, steering wheel torque, lateral acceleration, and yaw velocity, but a negative correlation between the muscles for middle deltoid (−0.75) and triceps brachii long head (−0.78) and these vehicle motions. The ANOVA test was used to analyze statistically significant differences in the main and interaction effects of muscle and vehicle speed. The mean absolute correlation coefficient exhibited an increasing trend with the increasing vehicle speed for the muscles (increasing rate%): upper trapezius (30.5%), pectoralis major sternal (38.7%), serratus anterior (13.3%), and biceps brachii (11.0%). The mean absolute correlation coefficient showed a decreasing trend with increasing vehicle speed for the masseter (−9.6%), sternocleidomastoid (−12.9%), middle deltoid (−5.5%), posterior deltoid (−20.0%), pectoralis major clavicular (−13.4%), and triceps brachii long head (−6.3%). The sternocleidomastoid muscle may decrease with increasing vehicle speed as the neck rotation decreases. As shoulder stabilizers, the upper trapezius, pectoralis major sternal, and serratus anterior muscles are considered to play a primary role in maintaining body balance. This study suggests that the primary muscles reflecting vehicle motions include the sternocleidomastoid, deltoid, upper trapezius, pectoralis major sternal, serratus anterior, biceps, and triceps muscles under real driving conditions. Full article

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios. Full article

This paper gathers the dynamical modeling of an unmanned aircraft and the design and simulation of the control system, allowing it to perform a Vertical Take-Off (VTOL) maneuver, a fixed-wing (FW) flight and a transition between the two configurations using two tilting rotors (Bi-Tilt). These Unmanned Aerial Vehicles (UAVs) operating in this configuration are categorized as Hybrid UAVs, for their capability of having a dual flight envelope: flying like a multi-rotor and navigating like a traditional fixed-wing aircraft, allowing the drone to perform complex missions where these two flight configurations are essential. This work exhibits the Bi-Rotor non-linear dynamics, valid for both flight configurations, the design of the control algorithm for stability and navigation, and a simulation of a complete flight mission. Full article

This paper investigates the application of contusion stability yaw control of a lightweight solar-electric vehicle. The vehicle’s customized design envelope makes it more sensitive to variations in load due to its low weight and relatively large size. To address this issue, control strategies were developed using differential motor torques to generate direct yaw moments using the vehicle’s rear in-wheel motors. This paper introduces the working conditions of solar vehicles and demonstrates the necessity of stability control. Vehicle parameters such as mass and center of gravity position are obtained to apply control to the real vehicle. The paper then describes two stability control strategies, using (i) sliding-mode control (SMC) and (ii) model predictive control (MPC). To account for the road bank angle of the test area and the impact of additional weight from a driver and passenger, a Kinematic-Based Observer is designed to estimate the vehicle’s side-slip based on measured values. To collect real-time data, a dSPACE MicroAutobox was installed on the solar vehicle. The results show the effect of the observer and controllers under different vehicle speeds and load conditions. Finally, closed-loop simulation results are presented to support the findings from the open-loop testing. Full article

Taking into account the issues faced by self-driving vehicles in multilane expressway scenarios, a lane-change decision planning framework that considers two adjacent lanes is proposed. Based on this framework, the lateral stability of an autonomous vehicle under near-limit conditions during lane change is studied by the phase-plane method. Firstly, a state-machine-based driving logic is designed and a decision method is proposed to design the lane-change intention based on the surrounding traffic information and to consider the influence of the motion state of other vehicles in the adjacent lanes on the self-driving vehicle. In order to realize adaptive cruising under the full working conditions of the vehicle, a safety distance model is established for different driving speeds and switching strategies for fixed-speed cruising, following driving, and emergency braking are developed. Secondly, for the trajectory planning problem, a lane-change trajectory based on a quintuple polynomial optimization method is proposed. Then, the vehicle lateral stability boundary is investigated; the stability boundary and rollover boundary are incorporated into the designed path-tracking controller to improve the tracking accuracy while enhancing the rollover prevention capability. Finally, a simulation analysis is carried out through a joint simulation platform; the simulation results show that the proposed method can ensure the driving safety of autonomous vehicles in a multilane scenario. Full article

Understanding the creation and development of a supercavitation bubble is essential for the design of supercavitational underwater vehicles and applications. The pressure field of the supercavitation bubble is one of the most significant factors in these processes, and it should be taken into account in the analysis. The underwater vessel is surrounded by a supercavitation bubble which is, in fact, an inhomogeneous fluid containing cavities (also described as microbubbles). The distribution of the cavities in the supercavitation volume dictates the pressure field and thus determines the stresses and forces that act on the vessel and affect its motion and stability. In this research, we suggest a new approach to studying the bubbles’ formation and learning about the cavities’ distribution in the low-pressure volume that envelops the underwater vehicle. We used Logvinovich’s principle to describe a two-dimensional ring of fluid that is created at the front edge of the supercavitation body and moves downstream along the vessel. To describe the distribution of the cavities we used Gillespie’s algorithm, which is usually used to describe biological and chemical systems. The algorithm succeeded in describing the random movement of the cavities in the cross-section under various conditions and also in describing their distribution and effects on the macroscopic system. A few factors of the physical characteristics of the fluid and the flow conditions were examined (the initial bubble supply, and the rate coefficients of creation and collapse). The results led to the conclusion that with an examination of those factors and using Gillespie’s algorithm, predictions of the distribution and thus the development of supercavitation could be achieved. The main finding of the analysis was that asymmetric development of the bubbles took place, in spite of the symmetry of the physical problem, as observed in high-resolution experiments. Full article

This paper presents an integrated active obstacle avoidance controller in the Model Predictive Control (MPC) framework to ensure adaptive collision-free obstacle avoidance under complex scenarios while maintaining a good level of vehicle stability and steering smoothness. Firstly, with the observed road conditions and obstacle states as inputs, a data-driven Gaussian Process Regression (GPR) model is constructed and trained to generate confidence intervals, as scene-adaptive dynamic safety envelopes represent the safety boundaries of obstacle avoidance. Subsequently, the generated safety envelopes are transformed into soft and hard constraints, incorporated into the MPC controller and rolling updated in the prediction horizon to further cope with uncertain and rapidly evolving driving conditions. Minimizing both the control increments and stability feature parameters are formulated into the objectives of the MPC controller. By solving the multi-objective optimization problem with soft and hard constraints imposed, control commands are obtained to steer the vehicle in order to avoid the obstacles safely and smoothly with guaranteed vehicle stability. The experiments conducted on a motion-base driving simulator show that the proposed controller manages to perform safe and stable obstacle avoidance even under hazardous conditions. It is also verified that the proposed controller can be applied to more complex scenarios with dynamic obstacles presented. Full article

In this paper, we address the problem of trajectory tracking control of autonomous vehicles by considering the nonlinear characteristics of tires. By considering the influence of the tires’ dynamics on steering stability, the proposed predictive controller can track the desired trajectory and desired velocity in the presence of road curvature while minimizing the lateral tracking deviation. First of all, a hierarchical control structure is adopted, in which the upper-level controller is used to calculate the desired acceleration and the desired front-wheel angle to maintain the control target, and the lower-level controller realized the command through the corresponding component devices. Moreover, a force estimator is designed based on the radial basis function (RBF) neural network to estimate the lateral force of the tires, which is incorporated into the boundary conditions of the vehicle envelope constraint to improve the adaptability of the controller to the vehicle performance. Finally, the proposed controller is tested by co-simulation of CarSim (a simulation software specifically for vehicle dynamics)/Simulink (a modular diagram environment for multidomain simulation as well as model-based design) and hardware-in-loop simulation system. The co-simulation and experimental results demonstrate the controller safely driving at the vehicle’s handling limits and effectively reduce the slip phenomenon of the vehicle. Full article

To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems. Expert pilots, however, demonstrate maneuvers that are deemed at the edge of plane envelope. Inspired by biological systems, in this paper, we introduce a framework that leverages methods in the field of control theory and reinforcement learning to generate feasible, possibly aggressive, trajectories. For the control policies, Dynamic Movement Primitives (DMPs) imitate pilot-induced primitives, and DMPs are combined in parallel to generate trajectories to reach original or different goal points. The stability properties of DMPs and their overall systems are analyzed using contraction theory. For reinforcement learning, Policy Improvement with Path Integrals ($P{I}^2$) was used for the maneuvers. The results in this paper show that $P{I}^2$ updated policies are a feasible and parallel combination of different updated primitives transfer the learning in the contraction regions. Our proposed methodology can be used to imitate, reshape, and improve feasible, possibly aggressive, maneuvers. In addition, we can exploit trajectories generated by optimization methods, such as Model Predictive Control (MPC), and a library of maneuvers can be instantly generated. For application, 3-DOF (degrees of freedom) Helicopter and 2D-UAV (unmanned aerial vehicle) models are utilized to demonstrate the main results. Full article

The clutch engagement process involves three phases known as open, slipping, and locked and takes a few seconds. The engagement control program runs in an embedded control unit, in which discretization may induce oscillation and even instability in the powertrain due to an improper scheduling period for the engagement control task. To properly select the scheduling period, a methodology for control–scheduling co-design during clutch engagement is proposed. Considering the transition of the friction state from slipping to being locked, the co-design framework consists of two steps. In the first step, a stability analysis is conducted for the slipping phase based on a linearized system model enveloping the driving and driven part of the clutch, feed-forward and feedback control loop together with a zero-order signal hold element. The critical period is determined according to pole locations, and factors influencing the critical period are investigated. In the second step, real-time hardware-in-the-loop experiments are carried out to inspect the dynamic response concerning the friction state transition. A sub-boundary within the stable region is found to guarantee the control performance to satisfy the engineering requirements. In general, the vehicle jerk and clutch frictional loss increase with the increase in the scheduling period. When the scheduling period is shorter than the critical period, the rate of increase is mild. However, once the scheduling period exceeds the critical period, the rate of increase becomes very high. Full article