IET Control Theory and Applications最新文献

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.

This article is concerned with the stability problem for discrete-time neural networks with a time-varying delay. For the two cases that the delay-variation bounds are known and unknown, new augmented Lyapunov–Krasovskii functionals (LKFs) are correspondingly constructed by fully considering the information on the state-related vectors and nonlinear activation function. Through the entire vector-extension method, the forward differences of the new LKFs are estimated to be affine with the delay. Relaxed stability criteria are consequently derived via the convex method. Two numerical examples are provided to show the effectiveness of the proposed method on conservatism reduction.

This paper investigates distributed online optimization in a networked multiagent system, where each agent has its own private objective and constraint functions that vary over time. In many real-world scenarios, computing the gradient of the cost function can be challenging, especially when agents have limited computational capabilities. Moreover, communication delays are common in practical networked systems due to various factors. This paper considers a unified framework for distributed online optimization that can handle bandit feedback and communication delays feedback simultaneously. A distributed primal-dual algorithm is proposed that utilizes bandit feedback, in which the agents estimate the gradients of their objective and constraint functions by sampling the function values. An enlarged network model that incorporates the delayed information exchanged among the agents is introduced. Through theoretical analysis, it is shown that the proposed algorithm achieves sublinear upper bounds on both the dynamic regret and the constraint violation despite communication delays.

The levitation control system is a critical subsystem in maglev trains, ensuring stable levitation of the train on the guideway. Achieving stable levitation requires providing the system with accurate levitation gap and corresponding velocity data while minimizing phase lag. This work proposes an enhanced tracking differentiator (TD) to reduce phase lag in both filtering and differentiation of input signals with varying noise levels. The improved performance is achieved by incorporating system damping and an amplitude factor into the control algorithm used to design the differentiator. Theoretical analysis guarantees the convergence of the proposed algorithm. Simulations and experiments conducted on the levitation gap data demonstrate the superior performance of the proposed TD in reducing phase lag for both filtered and differentiated signals. Furthermore, experimental results highlight the improved performance of the feedback controller when employing the proposed TD.

This study introduces an adaptive polynomial predictive filter (APPF) to address the issues of inconsistent and interrupted sensor data in estimation and prediction tasks. The APPF utilizes a polynomial prediction model to estimate time-variant/time-invariant sensor data through polynomial extrapolation. A key feature of the APPF is the autonomous determination of an accurate polynomial order, which reflects the nonlinearity level of the system. This dynamic adjustment allows the APPF to effectively capture the complex dynamics of nonlinear systems without manual tuning, thereby ensuring an accurate sensor data estimation and fusion. This study primarily focuses on validating the feasibility of the APPF method, and the theoretical basis, implementation, and performance evaluation of the APPF are covered, with results from MATLAB simulations and laboratory-based practical experiments comparing the APPF with Kalman filter techniques. Experiments using Hall effect sensors, which provide precise localization by detecting magnetic signals, were conducted to assess the effectiveness of the APPF in addressing the sensor data variances and disturbances commonly encountered in surgical robotic tracking. The findings demonstrate that the APPF significantly enhances estimation and prediction accuracy, highlighting its potential for improving sensor data reliability in various applications.

Undetected actuator faults on tilt tri-rotor UAVs can lead to system failures and uncontrolled crashes. Multiple flight modes result in complex models with strong nonlinearity, making fault detection of their actuators a very challenging task. To address this issue, this article proposes a fault detection method based on residual generated by using TSK fuzzy model. Initially, the flight modes of the tilt tri-rotor UAV are modeled as the TSK fuzzy model. Following this, the residual generator is employed for rapid detection of actuator failures. To enhance detection accuracy, the kernel principal component analysis (KPCA) algorithm is used for a secondary confirmation. The proposed algorithm was validated using both a simulation platform and real flight data. The results demonstrate that the fault detection algorithm achieves high accuracy and real-time performance, with a computing time of approximately 41 ms in real controller hardware, thus meeting the requirements of practical applications.

Control systems rely heavily on the accuracy and reliability of sensor data; however, the integrity of these data can be compromised through spoofing attacks, leading to significant modelling errors that can render control impractical. In addition, centralized control poses a significant threat to system security. To address these issues, a distributed framework is suggested for a discrete-time nonlinear system that encounters unknown dead-zones at its input. The framework uses the inherent resilience of a decentralized peer-to-peer network to secure information exchange, eliminating the need for prior knowledge of system dynamics or potential attacks. The proposed framework performs two complex tasks: identifying the nonlinear system and dealing with the unknown nonlinearity at the input in the form of a dead-zone. An adaptive dead-zone inverse is used to handle the unknown nonlinearity at the input in the form of a dead-zone and integrate blockchain technology to secure communication between components. The blockchain component ensures tamper-proof data transmission and resistance to cyberattacks, providing both detection and defence mechanisms without prior knowledge of system dynamics or potential attacks. The actuator and plant components are matched and synchronized using a private network with static nodes, ensuring deterministic and well-coordinated communication. Simulation results demonstrate that the proposed framework both with and without blockchain integration, maintains stability and outperforms traditional methods in terms of robustness and accuracy, even when all parts of the framework are adjusted in response to attacks.