IET Control Theory and Applications最新文献

This paper proposes an adaptive output-feedback boundary control scheme to stabilize the vibrations of the moving cage in the dual-cable mining elevator system assuming the damping coefficients of the cage axial and roll motions are unknown. The mathematical formulation of the system in the Riemann coordinates is described by a $��4�\times �4�$ hyperbolic partial differential equation (PDE) on a time-varying domain coupled with an ordinary differential equation (ODE) anti-collocated with the control input. At first, the nominal non-adaptive output feedback scheme is formulated by composing a state-feedback controller with the PDE state observer, utilizing the infinite-dimensional backstepping technique. Specifically, we apply two backstepping transformations to design the nominal state-feedback controller. This significantly facilitates the adaptive solutions of the backstepping kernel equations, when unknown parameters are replaced by their time-varying estimates. Then, a Lyapunov-based approach is followed to design the update laws for the unknown damping coefficients and to prove the closed-loop stability. It is shown that all states in the closed-loop system are uniformly bounded and the cage dynamics is asymptotically stable. A numerical simulation is presented to demonstrate the performance of the proposed controller.

This paper addresses the problem of heterogeneous multi-agent systems (HMAS), comprising multiple uncrewed ground vehicles (UGVs) and multiple uncrewed aerial vehicles (UAVs), collaboratively monitoring the wildfire in the presence of actuator faults and communication link faults during the fire monitoring mission. It presents a fixed-time fault-tolerant dynamic formation control scheme designed for HMAS, with the objective of monitoring either the circular or elliptical propagation of a wildfire. The paper adopts a fixed-time extended state observer (FxESO) to estimate the multi-source disturbances arising from external disturbances and actuator faults, ensuring fixed-time convergence of the estimation errors of the FxESO. By utilizing the Lyapunov candidate theorem, the collaborative tracking errors will converge to zero in fixed time, regardless of the initial position, ensuring that all agents in HMAS monitor the dynamic wildfire perimeter. Comparative simulation results are presented to illustrate the effectiveness of the proposed control scheme.

In this paper, an innovative error-based virtual composite-axis disturbance rejection backstepping control strategy is proposed for electro-optical tracking systems. Tracking accuracy cannot be improved by conventional composite axis structures where target position, velocity and acceleration are unknown and immeasurable. Our proposed method, however, operates without the need for target trajectory input signals or additional sensors. It solely relies on error information to adeptly simulate the compound axis system's functionality. Notably, its error suppression characteristics amalgamate dual-axis suppression features, substantially augmenting tracking performance. Moreover, to further optimize trajectory tracking and counteract the disturbances and uncertainties within the virtual composite axis, a backstepping control strategy is integrated with disturbance rejection. Remarkably, this approach achieves a 31.89% leap in tracking accuracy and a 73.87% boost in disturbance rejection performance. The effectiveness and superiority of the method have been thoroughly corroborated via simulations and experiments.

With the advancement of unmanned aerial vehicle (UAV) technology, research on adversarial interactions within UAV swarms has gained significant attention domestically and internationally. However, the existing decision-making algorithms are primarily tailored to homogeneous UAV swarm adversarial scenarios, facing challenges such as complex reward function design and limited decision-making timeliness when applied to more intricate scenarios. This article investigates the real-time control decision-making issues in UAV swarm adversarial interactions. First, an adversarial simulation environment for UAV swarms is constructed, which effectively unifies the environment and state representation, enhancing the response speed of our UAVs. Second, a distributed UAV swarm collaborative control algorithm based on multi-agent reinforcement learning is proposed, and an effective sparse reward function is designed to guide UAVs in adversarial gaming, making the UAV strategies more aggressive, enhancing the adversarial intensity, and further optimizing the control strategy to meet real-world demands better. Finally, the real-time performance and scalability of the proposed method are validated through simulations.

This paper investigates the problem of asymptotic stabilization for a class of uncertain nonlinear systems involving logarithmic quantization at the system input. Different from the existing results and approaches, a Lyapunov function candidate and an adaptive control law are developed to adaptively estimate the uncertain parameters and to asymptotically stabilize the uncertain nonlinear system, in which the control input also involves uncertain parameters, possibly in the nonlinear form. It is shown that asymptotic stabilization can be achieved under some mild conditions, even though the adaptively estimated parameters do not converge to the true system parameters. A sufficient condition is obtained for the asymptotic stabilizability, in terms of the quantization density and the multiplicative parameter error bound at the control input. More importantly, the proposed adaptive control law is suboptimal for the corresponding LQR control and achieves the $�H_{\infty }$-norm to be strictly smaller than $�\gamma$, provided that $��\gamma �>�1�$, for the uncertain linearized closed-loop system, effectively suppressing energy bounded disturbances. Finally, two simulation examples are worked out to illustrate the effectiveness of the proposed method.

This study outlines an approach to design an active fault tolerance control (FTC) system for satellite attitude systems that handle multiple actuator faults. First, a model is provided for the nonlinear attitude system of rigid satellites. Next, an actuator fault detection observer and a fault estimation observer are presented, which detects the time unknown actuator faults that occur and obtains estimated values. Using adaptive sliding mode control techniques, a fault tolerance attitude controller is designed that stabilizes the closed-loop attitude system of rigid satellites in the case of multiple actuator faults. Finally, an experiment is provided demonstrating the superior performance of the active FTC system proposed in this study.

Tractor-trailer wheeled mobile robots (TTWMRs) possess complex nonlinear dynamics that make their precise trajectory tracking control challenging. This paper explores an adaptive dynamic programming (ADP) approach that utilizes critic neural networks to improve tracking control for continuous-time TTWMRs. To achieve this, the decoupled kinematic and dynamic loops of the TTWMR are considered, and ADP controllers are proposed aimed at integrated trajectory and velocity tracking. Tis study defines two tracking error systems related to the kinematic and dynamic control loops, which reduces the computational load compared to previous research. The two critic neural networks approximate the optimal cost functions and enable the adaptive tuning of the control policies. Theoretical analysis demonstrates both closed-loop stability and convergence. Simulation results indicate that the proposed method offers superior tracking performance compared to earlier techniques, exhibiting lower errors and reduced control efforts. This underscores the advantages of using ADP to optimize the control of TTWMRs, even in the presence of partially unknown dynamics.

A power DC-DC Buck-Boost converter is controlled using a Lyapunov-based Adaptive Backstepping Control (ABSC) technique. It exhibits unfavorable behavior due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. This strategy is an enhanced iteration of the technique that uses the stability Lyapunov function to achieve greater stability and improved resistance to disturbances in real-world scenarios. Furthermore, the Black-box technique is employed to minimize the computing workload and facilitate implementation, under the assumption that there is no precise mathematical model available for the system. However, in real-time settings, disruptions with broader scopes such as fluctuations in supply voltage, variations in parameters, and noise might have adverse effects on the functioning of this approach. There is a need to set the most suitable initial gains for the controller to enhance its flexibility in more challenging working conditions. Therefore, to meet this requirement and enhance the effectiveness of the controller, the control scheme integrates a computational method called the Snake optimization (SO) algorithm. The SO method is known for its disciplined and nature-inspired approach, which results in faster decision-making and greater accuracy compared to other optimization algorithms. In order to further explain the advantages of this method, classical Backstepping and SO-based PID schemes are also developed and evaluated in various scenarios. The effectiveness of this approach is tested in both simulation and experimental environments, showing significant outcomes and lower sensitivity to error.

To reduce the typical time-consuming routines of plant modelling for model-based controller designs in Single-Input Single-Output (SISO) systems, the fictitious reference iterative tuning (FRIT) method has been proposed and proven to be effective in many applications. However, it is generally difficult to properly select a reference model without a prior information on the plant. This significantly affects the control performance and might considerably degrade the system performance. To address this problem, a pseudo-linearization (PL) method using FRIT is proposed, and a new controller for SISO non-linear systems by combining data-driven and model-based control methods is designed. The proposed design considers input constraints using model predictive control. The effectiveness of the proposed method was evaluated based on several practical references using numerical simulations for hysteresis and dead zone classes and experiments involving artificial muscles with hysteresis characteristics.

The most common cause of mechanical failure is bearing failure, and the characteristics of each failure correspond to a certain degree of severity. This paper proposes a fault diagnosis model for detecting motor bearings. The model uses three steps: feature extraction, feature selection, and classification. In feature extraction, empirical mode decomposition, fast Fourier transform, and envelope analysis extract important features from the signals measuring the motor. In feature selection, a binary differential evolution and binary whale algorithm are developed and the storage space is increased to eliminate irrelevant features again. Finally, KNN and SVM are used to determine the stability of the bearing fault diagnosis model.