IET Control Theory and Applications最新文献

Electric vehicles (EVs) with distributed drive configurations demonstrate improved energy storage potential through battery-dominated systems, enabling independent torque allocation across individual wheels. This paper proposes a differential torque control framework for distributed-drive electric vehicles to enhance trajectory tracking accuracy and yaw stability during double-lane change maneuvers. A hierarchical control architecture with three layers are developed, integrating model predictive control with quadratic programming-based torque allocation to coordinate longitudinal velocity tracking and lateral path following. The lateral controller generates real-time differential torque commands (front-rear axle torque variation range: $��\pm �282.68�$–$��\pm �409.42��N�·�m�$) through a 3-DOF vehicle dynamic model, while the longitudinal controller maintains speed errors below 0.1 m/s through four-wheel independent torque regulation. Co-simulation on the CarSim-Simulink platform demonstrates the controller's adaptability to road friction coefficients ($��\mu �=�0.5�,�0.8�$) and speed conditions ($��u�=�40�,�50�,�60�$ km/h). The results achieve maximum yaw rate stabilization at 0.38 rad/s during high-speed maneuvers. Simulation results reveal that despite lateral deviation amplification (80–160 m trajectory segments) and torque oscillation divergence under $��\mu �=�0.5�$

This article proposes a data-driven model predictive control (MPC) method for multirotor collision avoidance, considering uncertainties and the unknown dynamics caused by a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is employed to derive the governing equations of the multirotor system. SINDy is capable of discovering the equations of target systems from limited data, under the assumption that a few dominant functions primarily characterize the system's behavior. In addition, a data collection framework that combines a baseline controller with MPC is proposed to generate diverse trajectories for model identification. A candidate function library, informed by prior knowledge of multirotor dynamics, along with a normalization technique, is utilized to enhance the accuracy of the SINDy-based model. Using data-driven model from SINDy, MPC is used to achieve accurate trajectory tracking while satisfying state and input constraints, including those for obstacle avoidance. Simulation results demonstrate that SINDy can successfully identify the governing equations of the multirotor system, accounting for mass parameter uncertainties and aerodynamic effects. Furthermore, the results confirm that the proposed method outperforms conventional MPC, which suffers from parameter uncertainty and an unknown aerodynamic model, in both obstacle avoidance and trajectory tracking performance.

In this paper, we present a new method to determine the continuous-time response of sampled-data systems with uniform sampling, zero-order hold, and full-state feedback. In such systems, a continuous-time plant is controlled using a discrete-time control law. Traditionally, sampled-data systems are designed in discrete time, resulting in, given by the nature of this kind of modelling, unmodelled intersample behaviour. We show that the Laplace transform of the otherwise piecewise-continuous state response can be expressed in closed form that fully represents the intersample dynamics. A practical technique is also provided to decouple individual vibration components and reconstruct response functions in the time domain. The proposed approach is also able to capture intersample vibrations compared to common methods, which may lead to inaccurate results in specific cases. The presented new formulae are derived analytically and verified by simulations through numerical examples and experiments on a DC motor drive.

This paper investigates the cooperative time-varying formation fault-tolerant control problem for multiple quadrotors with unknown actuator faults and full state constraints. In order to ensure the safety and operability of quadrotors in the confined flight environment, a novel transformed function is first introduced to convert the original quadrotor systems into unconstrained equivalent systems, which increases the flexibility of the controller design. Then, a distributed kinematic control protocol and fault-tolerant dynamic control protocol using the adaptive neural networks estimation technique are developed to guarantee the cooperative time-varying formation of multiple quadrotors subject to uncertain parameters. Meanwhile, the unknown actuator loss of effectiveness and bias faults are compensated and the state variables of position subsystem and attitude subsystem can be maintained within the designed performance constraint sets even when actuator faults occur. Via Lyapunov stability theory, the cooperative formation fault-tolerant performance analysis is presented. The proposed control strategy is validated through simulations.

Suspended constant force (SCF) control is a critical technology in suspended gravity offloading systems. However, inherent underactuation, unmodelled dynamics, and external disturbances can significantly degrade control performance and even compromise system stability. In this article, pneumatic artificial muscle (PAM) actuators are used as a replacement for traditional passive dampers to address the underactuation problem. Additionally, we propose a novel systematic robust output feedback model predictive control (ROFMPC) framework, which incorporates a radial basis function neural network (RBFNN)-based model compensator, a Luenberger state estimator, and a tube model predictive controller. The RBFNN-based model compensator compensates for unmodelled dynamics, while the Luenberger state estimator observes external disturbances. The model predictive controller then generates the optimal control sequence. Analytical results indicate that our designed SCF system encounters similar control challenges as those in antagonistic PAM (APAM). Therefore, sufficiently comprehensive numerical simulations and physical experiments are conducted on the APAM platform to verify the effectiveness of the proposed control framework. These results demonstrate that the proposed ROFMPC framework significantly improves force trajectory tracking performance for constant force control.

Designing water level control for a U-tube steam generator (UTSG) in nuclear power plants (NPP) remains a challenge, especially at low power demand due to unreliable steam flow measurements. This paper addresses the steam flow rate as a disturbance to the plant, treating it as an inaccessible variable. To estimate the disturbance (steam flow rate) and system states, an adaptive integral terminal sliding mode observer is developed. These estimated values can be utilized in the water level control design to enhance the reliability and performance of the control system. An adaptive observer is developed such that the augmented systems formed by the error dynamical systems and the designed adaptive laws are globally uniformly ultimately bounded. This technique is applied to a non-minimum phase system model representing the UTSG system to improve water level control and prevent possible serious consequences. Various disturbance signal forms with different amplitudes are simulated to demonstrate the reliability of the proposed technique. The simulation results show the effectiveness of the method proposed in this paper.

Model predictive control (MPC) is advantageous for autonomous vehicle path tracking but suffers from high computational complexity for real-time implementation. Event-triggered MPC aims to reduce this burden by optimizing the control inputs only when needed instead of every time step. Existing works in literature have been focused on algorithmic development and simulation validation for very specific scenarios. Therefore, event-triggered MPC in real-world full-size vehicle has not been thoroughly investigated. This work develops event-triggered MPC with switching model for autonomous vehicle lateral motion control, and implements it on a production vehicle for real-world validation. Experiments are conducted under both closed road and open road environments, with both low speed and high speed maneuvers, as well as stop-and-go scenarios. The efficacy of the proposed event-triggered MPC, in terms of computational load saving without sacrificing control performance, is clearly demonstrated. It is also demonstrated that event-triggered MPC can sometimes improve the control performance, even with less number of optimizations, thus contradicting to existing conclusions drawn from simulation.

This paper presents a comprehensive review of solar panel performance degradation in both industrial and residential sectors. Drawing on a wide range of academic studies, the paper systematically analyses the key factors affecting the performance of photovoltaic (PV) systems to provide in-depth understanding of degradation mechanisms along with effective countermeasures. These factors include the selection and properties of the materials used in PV panel manufacturing, changes in environmental conditions, the inherent degradation rate of materials and user behaviour. The paper aims to comprehensively reveal the mechanisms by which environmental and human factors contribute to PV panel performance degradation, assess their impact on the operational efficiency of the power systems and explore feasible adaptive solutions to mitigate or restore PV system performance. The paper also incorporates a technical framework aligned with the IEC 61850 standard and provides constructive recommendations for enhancing the efficiency and reliability of renewable power systems.

The paper holds substantial theoretical and practical significance. At a macro level, it contributes to reducing the overall cost of PV energy production while minimising investment in equipment maintenance and human resources. At a micro level, it enhances the utilisation efficiency and basic performance of PV systems. The recommendations of this paper not only support the sustainable growth of the renewable energy industry but also facilitate the synergistic expansion of the upstream and downstream industrial chain, fostering new employment opportunities and business potential. For individual users, businesses and the public sector, the paper provides a robust scientific foundation for developing future energy strategies with practical insights to advance global sustainable development goals.

Robotic manipulators are complex mechanical systems that exhibit highly nonlinear dynamics and subject to various forms of disturbances such as friction, external forces and other unmodelled dynamics. Mathematical models representing robot dynamics are extensively used for design, simulation and control purposes, and can be derived through analytical and experimental methods. Dynamic behaviours predicted by mathematical models often deviate from the observed dynamics of robot manipulators because of external disturbances, parametric uncertainties, and unmodelled dynamics. The observer-based control that eliminates the need for highly accurate system modelling is an appealing approach for robot control. This paper introduces an extended state observer-based structure that can be either utilized as a stand-alone controller or implemented within a model-based adaptive control scheme. The proposed control scheme allows the implementation of extended state observers independently of the availability and quality of the dynamic model. The stability of the proposed controllers in presence of model uncertainties and generalized disturbances is investigated through the Lyapunov analysis. The experiments performed on a six-DoF industrial robot validate the theoretical stability results. Evaluation of performances of the proposed controllers in various operating conditions are presented in a comparative manner. Experimental results show that the extended state observer-based controller outperforms the adaptive controller in trajectory tracking performances.

This paper investigates the output feedback containment control problem for multi-agent systems (MASs) with multiple leaders under denial-of-service (DoS) attacks. In the considered system, all agents are modelled by partial differential equations. To address the issue of unmeasurable system state, a distributed observer is advanced. The distributed controller is designed based on observer state, enabling containment control of the cluster affected by DoS attacks. As a result, consensus can be achieved between leader agents and follower agents over an undirected graph. Furthermore, the tolerable frequency and duration of DoS attacks are outlined. Using Lyapunov techniques and mathematical inequalities, sufficient conditions for matrix inequalities are proven to ensure global asymptotic stability of MASs under DoS attacks. Finally, the efficacy of observer and controller is validated through illustrative examples.