IET Control Theory and Applications最新文献

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.

This study presents the design of a decentralized terminal sliding-mode (TSM) controller to solve the trajectory tracking problem of a composite robotic device made up of two-dimensional Cartesian and multiple-degree-of-freedom robotic manipulators. The dynamics of the proposed composite robotic device satisfy a standard Lagrangian structure affected by the modeling uncertainties related to the internal interconnection between joint motion and external perturbations. The set of adaptive gains included in the controller implies enforcing the finite-time convergence of the tracking error (TE) to an invariant region considering the state bounds describing the restricted motion of all joints. The application of the barrier Lyapunov stability analysis theory addresses the previously known state constraints for both devices, considering the inclusion of a time-varying gain that guarantees the ultimate boundedness of the TE even with the presence of the effect of external perturbations. The suggested controller was evaluated using a virtual representation of the composite robotic device, which showed better tracking performance (while the restrictions were satisfied) than the performances obtained with the traditional linear state feedback and first-order sliding-mode controllers with restrictions. Analyzing the mean square error and its integral confirmed the benefits of using the adaptive barrier control to satisfy the TSM form.

This paper addresses the finite-time trajectory tracking control problem for quadrotor UAVs under model uncertainties, external disturbances, and input saturation. A robust finite-time trajectory tracking control scheme is proposed by following steps. First, a nominal controller is established based on integral terminal sliding mode control. Second, an auxiliary system is used to address the input saturation constraint problem. It effectively restricts inputs from exceeding the bounds. Third, a reinforcement learning component is designed to estimate and compensate for model uncertainties and external disturbances. Then, a robust finite-time scheme is constructed by integrating the nominal controller, the reinforcement learning compensating component, and the auxiliary system. Theoretical analysis verifies that the finite-time stability of controlled systems can be guaranteed by the proposed tracking control scheme, and the tracking error can be driven to a compact set in finite time. Furthermore, simulation results confirm the effectiveness of the proposed control scheme.

This paper investigates the long-term maritime patrol mission conducted by multiple fixed-wing unmanned aerial vehicles (UAVs). Based on the concept of ‘routine operation’, the task scenario for long-term maritime patrol mission is defined. Subsequently, a novel optimization scheduling model for the multi-UAV long-term maritime patrol mission is established. This model is a nonlinear multi-objective optimization model. Given the characteristics of this model, it is further linearized to transform the nonlinear model into an equivalent linear multi-objective 0–1 integer optimization model, facilitating easier solving. Two simulation cases are conducted to validate the proposed model and the solving method. The simulation results demonstrate the effectiveness of the proposed approach.

This paper develops a matrix-separation-based Lyapunov functional method to study the extended dissipativity analysis and synthesis issue of discrete-time Lur'e-type delayed systems. The advanced idea of matrix-separation is reflected in Lyapunov functional candidates and estimates summation terms as precisely as feasible. First, we introduce a novel summation inequality based on the matrix-separation method to provide a bound for the augmented summation that involves common state variables. An improved delay-product-type Lyapunov functional is devised by extending non-positive definite summations as separate subblocks of the state-augmented summation. Then, by using the matrix injection method to handle the cubic delay term in the functional forward difference, a delay-variation-dependent stability criterion and an extended dissipativity criterion are developed under the matrix-separation-based method. Subsequently, sufficient conditions for the control design of Lur'e-type systems are derived. Finally, the effectiveness and superiority of the proposed method are verified through its application to Chua's circuits, neural networks, and a stochastic numerical example.