IET Control Theory and Applications最新文献

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.

Visually assisted unmanned aerial vehicle (UAV) autonomous landing has drawn a lot of interest as a vital technology with the quick development of UAV systems. From the perspective of robustness, a visual servoing disturbance rejection landing (VSDRL) scheme based on fractional-order linear active disturbance rejection control is novelly proposed to achieve the disturbance-resistant landing. The proposed VSDRL scheme is constructed by two modules: (i) A visual positioning algorithm combining YOLOv5 with Kalman filtering to solve the occlusion problem in the visual positioning module obtaining the relative position relationship; (ii) On the basis of linear active disturbance rejection control, fractional order is introduced to improve the antidisturbance ability and response speed. Theoretical analysis, computer simulations and real UAV experiments all verify the effectiveness and superiority of the proposed VSDRL scheme.

The integration of renewable energy resources in modern power systems promotes flexible demand response but poses challenges to power balance and frequency stability due to their intermittent generation. To address this problem, this study deals with the frequency control of power system in the presence of demand responses. The dynamic electricity pricing scheme enabling both demand response participants and suppliers to contribute to the frequency regulation via their own decision-making process is proposed. The main concern in the controller design is the integration of physical and human systems on the same timescale, encompassing controllers based on frequency dynamics and dynamic pricing. Therefore, event-triggered conditions are proposed to decrease the communication frequency while ensuring system stability, leveraging the passivity property of the system. Under the proposed event-triggered conditions, the authors clearly demonstrate the asymptotic stability around the equilibrium point of the entire system. Furthermore, a numerical simulation using a four areas power network system is performed, confirming the effectiveness of the proposed control scheme and the stability of the system.

The roller kiln with multi-temperature zones used for cathode material sintering is an interconnected system with time delay in energy transfer and precise control of the temperature for the preparation of cathode materials for lithium-ion batteries. However, the interconnection between the temperature zones, the time delay of the temperature state, and the disturbance of the external environment make it difficult to control the sintering process. For this reason, this paper develops a distributed $�H_{\infty }$ control of the temperature of roller kiln based on adaptive dynamic programming (ADP). Firstly, the heat transfer mechanism of sintering process is discussed; the law of energy conservation provides the physical basis for the sintering process on which the autocorrelation function method identifies the time delay. Then, the cost function is constructed by combining the Lyapunov–Krasovskii function containing the time delay, the temperature state, the heating power of the silicon carbon rod and the perturbation. Subsequently, Hamilton–Jacobi–Isaac equation with the optimal cost function and the optimal distributed control strategy are approximated by neural network of ADP. Finally, the stability of the closed-loop system is proved by Lyapunov functional analysis, and the effectiveness of the proposed method is verified by the simulation results of roller kiln temperature control.

To suppress the non-linear motion for a class of strict-feedback fractional order non-linear systems, and to improve their transient and steady-state performance, a neuro-adaptive prescribed performance backstepping control strategy suitable for a class of strict-feedback non-linear systems is proposed in this paper. Firstly, the interval Type-2 fuzzy neural network is constructed to approximate the unknown non-linear functions. Secondly, the tracking differentiator is introduced to address the problem of ‘explosion of complexity’ associated with the technique framework of backstepping. Then, a prescribed performance backstepping controller composed of predetermined performance functions and equivalent transformed errors, which can ensure that the tracking errors converge with the predetermined performance intervals for a class of strict-feedback non-linear systems, is designed within the technique framework of backstepping control. Finally, the stability analysis, two simulation experiments and comparative experiment results are presented to demonstrate the feasibility and effectiveness of the designed controller.

This study addresses the end-effector position tracking control of robot manipulators actuated by brushless direct current (BLDC) motors under the constraint that the entire electromechanical system (i.e. kinematic, dynamic, and electrical) contains uncertain parameters, and both joint velocity and Cartesian space velocity measurements are unavailable. Specifically, a partial state feedback controller (requiring phase current measurements of BLDC motors) is presented in conjunction with a novel robust velocity observer formulation that achieves semi-global uniform ultimate boundedness of the Cartesian space position tracking error. The stability of the closed-loop system is ensured via Lyapunov-type arguments. To demonstrate the efficacy of the proposed design, an experimental study is conducted on an in-house developed, two degree of freedom planar robot manipulator driven by BLDC motors.

To solve the problem of state and bias estimation for the non-linear system with non-Gaussian noise terms, a series of exogenous Kalman filters (XKF)-based state and bias estimation algorithms are proposed. First, a non-linear system model with bias and non-Gaussian process noise is established. Second, the l₁ norm is incorporated into the two-stage exogenous Kalman filter (TSXKF) algorithm, known as the l₁-TSXKF, to mitigate the effect of non-Gaussian process noise. Considering the uncertainty in the system transition model, adaptive factors are introduced into the Kalman filter, known as the adaptive Kalman filter algorithm, to enhance the estimation accuracy by modifying the predicted state covariance. Then, the l₁ norm adaptive two-stage exogenous Kalman filter (l₁-ATSXKF) algorithm is designed by combining the advantages of the l₁ norm, adaptive factors and XKF in dealing with non-Gaussian process noise, model uncertainty and state/bias simultaneous estimation, respectively. Finally, the simulation results show that the proposed algorithms can reduce the influence of the non-Gaussian characteristics of the system and obtain high-precision attitude and bias estimation results quickly in the process of satellite attitude manoeuvre, which is conducive to the application of satellite in orbit.