Measurement and Control

In this study, the design of Sliding Mode Control (SMC) has been carried out based on a second order reference model with a specified transfer function. The primary objective of this study is to determine the SMC controller parameters according to the desired time response in the output signal. The aim is to determine the optimal control gains that minimize the difference between the reference and plant outputs in a systematic way. By minimizing the error between the reference model and the SMC controlled system model, the optimal SMC parameters, specifically the sliding surface slope ( λ ) and the switching function coefficient ( K ), are obtained. Integral performance criteria were used to minimize the error. Both the reference system and the system controlled by the SMC have second-order transfer functions. While the natural frequency ( ω n ) in the reference model system remains constant, the damping ratio ( ζ ) was varied between 0.5 and 1. For each ζ value in the reference system, the optimal SMC parameters ( λ and K ) were obtained by using the Fmincon optimization algorithm to minimize the error between the two systems based on the selected integral performance criteria. By applying the obtained SMC parameters to the controlled system, unit step responses were obtained in the output of the controlled system. T optimization process was performed based on the transfer functions of two different known systems, and the variation of the optimal SMC parameters according to the damping ratio of the reference model was presented in tables and graphs. The simulation results confirm that the proposed method significantly improves the accuracy and robustness of tracking compared to conventional SMC tuning approaches. These results demonstrate the effectiveness of the proposed method for designing reliable and efficient controllers for second-order dynamic systems. The success of the reference model-based second-order system controlled by the SMC method is clearly demonstrated by the generated graphs and tables. This study introduces a new optimization-based SMC design approach by establishing an explicit analytical relationship between the damping ratio and the controller parameters. In the proposed method uses regression, which avoids the need for iterative tuning and repeated calibration.

Autonomous driving is a complex system which includes perception, cognition and control functions. Environmental perception represented by target detection is an essential part of autonomous driving technology. To improve the performance of target detection, the model is constructed based on YOLOv3 and improved with three methods to detect the obstacles of vehicles/cyclists/pedestrians on the road. The double population genetic algorithm is used to enhance the K-means clustering analysis, which is named K-IGA algorithm to optimize the value of anchor box. According to the structure of the convolution module, the Batch Normalization (BN) layer is merged into the convolution layer to obtain a new output function. The calculation of the loss function is improved using the Generalized Intersection over Union (GIOU). Then, the training set and test set are made based on the data set KITTI. The YOLOv3 algorithm with different improvement methods is named MD2, MD3, MD4. They are trained and tested on the data set with the classical YOLOv5 algorithm. The results show that the MD4 algorithm with all the above three improvement methods has the highest accuracy. Compared with the benchmark model MD1 and YOLOv5. Its mAP is 7.8% higher than MD1′s and 0.55% higher than YOLOv5′s. The test results show that the detection model can effectively identify vehicles, pedestrians and cyclists in the road scene. The real-time performance meets the requirements of target detection during vehicle driving.

This essay investigates the issue of adaptive fuzzy fixed-time control for a type of stochastic nonlinear systems, inputs of which are perturbed by saturation. First of all, an improved practical fixed-time stable criterion is presented for stochastic nonlinear system. Then, Gaussian error function (erf) and fuzzy logic systems (FLSs) are successively unitized to handle the issue of input saturation and unknown functions for the considered systems. Based on the given criterion and adding a power integral method, an adaptive fuzzy fixed-time controller is designed, which can ensures that all the variables converge to a compact set containing equilibrium point within a fixed time. Eventually, the simulation result of the numerical example and the one-link manipulator system demonstrate the validity of the suggested control strategy.

Detecting misfires in internal combustion engines (ICEs) is essential for maintaining engine health, reducing emissions, and improving performance. However, current misfire detection systems are often generic and lack the capability to localize faults to specific cylinder banks. This limitation is primarily due to the high cost of piezoelectric sensors and the complexity of associated data processing, which restricts widespread adoption, especially in cost-sensitive applications. To address this challenge, the present study explores the use of cost-effective microelectromechanical system (MEMS) sensors for real-time misfire detection. Vibration data from a production Hyundai Xcent MPFI engine is collected and analyzed using decision tree machine learning classifiers. Three types of features—statistical, auto-regressive moving average (ARMA), and histogram-based—are extracted from the MEMS data. The J48 decision tree classifier, when applied to selected histogram features, achieves 100.00% classification accuracy which in turn exudes its effectiveness in the detection and specific localization of misfires. This result is found to exceed the performance level investigated in studies with high classification accuracies averaging between 99.00% and 99.80%, including methodologies spanning transfer learning models for similar applications. This approach offers a low-cost, high-performance solution suitable for on-board engine diagnostics. Furthermore, this approach provides a framework that prospectively enables a broader integration of advanced misfire detection in ICE applications.

ISO 25178-600 is an international standard explaining metrological characteristics that are applied to calibrate the optical areal surface topography measuring instruments. The determination of measurement noise is typically one of the metrological characteristics to be estimated for calibrating areal surface topography measuring instruments. However, the metrological characteristic estimation, that is, specific to the vertical heights of the optical instrument stage, requires calibration time. Commonly, the estimation of measurement noise is only applied for a specific range from the total vertical range of an instrument and measurement parameters. The noise can significantly affect and increase the measurement uncertainty of measurements. Reducing measurement noise is crucial to reduce the overall measurement uncertainty and then improve the measurement reliability. Calibrating the measuring instruments can help quantify this noise contribution to the uncertainty and hence can develop good measurement practices on how to minimize or reduce the measurement noise. Machine learning (ML) can estimate measurement noise for uncalibrated vertical axis heights by using known measurement noise from a specific vertical range and specific measurement parameters. This process of machine learning estimation can significantly reduce the overall time of performing the measurement-noise calibration for all vertical axis ranges of optical measuring instruments. This paper applies Bayesian machine learning to predict the measurement noise of a focus variation microscopy (FVM) on overall height locations based on measurement noise estimations from a few specific height locations of the instrument. This method can reduce the overall measurement operational cost. Different measurement parameters are used to quantify the measurement noise and the prediction test with the proposed machine learning method. The parameters include vertical and lateral resolutions, image contrast, exposure time, objective lens, illumination type and measurement position of the vertical motion stage axis. The measurement noise estimation is performed by measuring a standard flat-surface artefact and applying a subtraction method. The Bayesian machine learning estimation is optimized by finding the optimal hyper-parameter of the Bayesian model. Results show that Bayesian machine learning can predict unknown measurement noise at different vertical heights and with different measurement parameters at the accuracy up to 94%. In addition, the uncertainty of prediction is provided for reliable comparison with known data.

In addressing the challenges posed by significant time delays and multiple disturbances in low-pressure vacuum systems, this paper proposes an anti-disturbance Smith predictor compensation control method. The method, based on the dynamic characteristics of the low-pressure vacuum system, integrates the synergistic advantages of feedforward and feedback control to design a composite controller with parameter self-adaptive tuning (AFFPI), which optimizes control parameters according to changes in the process state. Secondly, to address the challenge posed by the inherent lag characteristics of the vacuum system on control performance, a Smith predictor (SP) structure based on dynamic compensation was established in the internal loop, effectively solving the issue of slow response caused by significant time delays in the low-pressure vacuum system. Finally, an anti-disturbance filtering unit was introduced into the pressure feedback channel. By configuring appropriate filter gain factors, the unit alleviates fluctuations in the control signal caused by disturbances to the SP, preventing the performance degradation typically observed in traditional SP control methods under disturbances, thus achieving efficient and high-precision pressure regulation in the low-pressure vacuum system. The simulation results demonstrate that the proposed control method reduces the average settling time by 31.73 s, reduces the overshoot by 10.59%, and lowers the ITAE index by 1619.41 when compared to AFFPI, Mac-PID, and PID-Smith. Under four types of simulated disturbances, the method demonstrates the most optimal control performance, exhibiting an average enhancement of 19.66% in disturbance control performance in comparison to the Smith predictive control method that does not consider disturbances (AFFPI-Smith). The experimental findings demonstrate that the proposed method reduces the average settling time by 7.32 s and decreases the CV, TV, ITE, and ITAE indices by 1.8247 × 10⁻⁵, 0.385, 3.85, and 26.23, respectively. This results in improvements in control accuracy, control time, and stability. Specifically, compared with AFFPI-Smith, the ITAE is reduced by 4.72, and the disturbance performance is improved by 11.12%. The proposed method maintains excellent control performance even when process conditions or control target pressures are adjusted, and it demonstrates strong robustness and disturbance rejection capability.

When there is a relative difference in the delay scale of different frequency component signals input to a variable fractional delay filter, adjusting a certain sub filter parameter alone cannot flexibly adjust the filter group delay, resulting in distortion errors in the signal spectrum at the output of the filter. An optimization design method for dynamically adjustable variable fractional delay filter is proposed for this purpose. It generates the delay response frequency of an ideal variable fraction filter under the Farrow structure. Based on trapezoidal curves, the multi-scale Fourier spectrum interpolation method is used to linearly combine the sub filters in the Farrow structure to reduce group delay distortion errors. Then, it derives the polynomial parameters of the Farrow structure’s sub filters, and optimizes the sub filter parameters uniformly. The method performs real time calculation and updating of adjustable parameters through PID algorithm to optimize and adjust the fractional delay parameters of VFD filters, achieving flexible and adjustable group delay of VFD filters. Through experiments, it has been proven that the VFD filter designed in this paper has low maximum amplitude error, normalized root mean square error, and maximum group delay error. The amplitude frequency response and group delay response can be adjusted flexibly, and it has good application effects.

In the domain of mobile robots (MRs) utilizing multi-wheel directional control, position synchronization in steering wheels remains a challenging problem, particularly when implemented in permanent magnet steering wheels for vertical tank robots. Addressing this critical problem, this paper presents a position synchronization control strategy that integrates a mean deviation coupling strategy (MDCS) with state feedback tracking control (SFTC) enhanced by integral action. In essence, MDCS minimizes position synchronization errors, especially during deviations or fluctuations, while SFTC enables fast responses to state changes and improves adaptability in uneven terrain. Combined with integral action, SFTC compensates for static errors, enhancing long-term stability. Furthermore, to establish robust evidence for the proposed strategy, this paper validates the results through both simulation and experimental analysis. Initially, simulation results show that integrating the MDCS with SFTC improves settling time, reducing it from 0.6 to 0.4 s. Additionally, experimental tests demonstrated that the improvement in settling time closely matched the simulation results during the reference angle change phase, excluding the initial startup phase. Without the synchronization strategy, the settling time was 0.5 s, whereas applying the control strategy increased it to 0.7 s. This discrepancy arises from the selected MDCS control coefficients, which were not well-suited to the actual system, leading to ineffective compensation signal intervention. As a result, the system dynamics were altered, and the stability of the poles was no longer ensured compared to the original selection. It can be concluded that the integration of MDCS with SFTC holds strong potential for practical application in steering wheels, improving synchronization while reducing the computational burden on hardware.

As robots are required to conduct versatile manipulations in unstructured space environments, traditional planning and control strategies may become cumbersome or even infeasible. To overcome this challenge, the paper presents imitation learning with inherent Lyapunov stability (IL2S), a novel framework for jointly learning the dynamical system and accompanying Lyapunov function from demonstrations. We represent the robot motion policy as a nonlinear autonomous dynamical system that captures the invariant motion patterns from a handful of teaching examples. Furthermore, the elaborate neural networks are leveraged to simultaneously learn the motion model and the parametric control function, whereby the generated movements closely follow the demonstrations, ultimately converge to the target, and instantly respond to unanticipated changes. Our approach yields resembled trajectories on the handwriting dataset and is demonstrated extensively in real-world experiments, where the robot accomplishes two different satellite manipulation tasks, namely static grasping and dynamic docking.

Aiming at the problems of unknown disturbances and actuator failures during the flight of quadrotor unmanned aerial vehicle (UAV), a non-singular predefined time sliding mode controller is designed for quadrotor UAV with actuator failures. Firstly, the actuator failure model is analyzed, and the quadrotor UAV with failure model is constructed. Secondly, the uncertain terms, external disturbances, and actuator failures are regarded as lumped disturbances, and the predefined time disturbance observer is designed to estimate the lumped disturbances. The designed observer can ensure the tracking of the lumped disturbances within the predefined time. Finally, a non-singular predefined time segment sliding surface and an improved segmented exponential convergence law are designed. The proposed sliding surface avoids the singular problem caused by the traditional predefined time sliding surface, while the segmented exponential convergence law suppresses the chattering of the control input. The stability of the designed observer and controller was proved through Lyapunov theory. The simulation experiments show that under the proposed control method, the tracking performance of the quadrotor UAV can complete convergence within the predefined time. Meanwhile, during the actuator failure period, the proposed control method outperforms the traditional predefined time sliding mode control and non-singular terminal sliding mode control in terms of robustness.