Measurement and Control

Defects such as cracks can cause dangerous damage to the metal structure and may lead to structural collapses. Cracks can exist in various shapes and sizes where they can start to develop from small scale lower than 1 mm and spread to contribute to the complete fracture of components. Hence, early discovery and monitoring of any cracks in their early stage are crucial to prevent any sudden fatal accidents in the future. This work presents the study and detailed analysis of an ECT probe’s development based on AMR sensors to identify sub-millimeter surface cracks in galvanized steel plates. The probe consists of an excitation coil that induces an eddy current in sample plates and two AMR sensors that detect the differential eddy current-induced magnetic response. A phase-sensitive detection technique with a lock-in amplifier is used to evaluate the magnetic field distribution detected by the AMR sensors. The measured magnetic responses are classified to the depth, width, length, and complex shapes of artificial slits, and the probe is used to perform line scans and 2-D map scans above the slits’ positions. The probe was able to characterize slits with a depth and width as low as 210 and 50 µm, respectively, by using an excitation current of 4 mA at 1 kHz. The slit orientations that were perpendicular to the differential direction of the AMR sensors were clearly visualized, with their estimated lengths showed a good correlation with the physical slit lengths. In the future, the developed system can be expected to help towards the development of a more sophisticated crack detection system where real-time inspections can be realized and applied in various fields.

Polygonalization of the wheel describes the growth of out-of-round profiles of the wheels of railway vehicle. This problem was identified in the 1980s but its mechanism is still not well understood. The wheel-rail disturbance formed by wheel polygonalization will accelerate the fatigue fracture of the key parts of rail vehicles and seriously threaten the safety of rail vehicle. This fact has led to significant efforts in detecting and diagnosing wheel polygonalization, in particular in setting the criteria for health monitoring. Currently, the time-domain feature parameters extraction method based on data statistics and frequency-domain feature parameters extraction method based on spectrum estimation are widely applied to detect wheel polygonalization. However, the basis of spectral estimation is the Fourier transform, which is not good at dealing with non-linear vibration systems (such as vehicle-track coupled system). Aiming at the wheel polygonalization problem existing in high-speed train, the non-linear extent of vibration response of vehicle system caused by wheel polygonalization is analyzed based on vehicle-track coupled dynamics and adaptive data analysis method. A typical high-speed train model is established according to the vehicle-track coupled dynamics theory. The wheel polygonalization model is introduced and vehicle system vibration response is calculated by numerical integration. The vibration response signal is decomposed by empirical mode decomposition (EMD) to produce the intrinsic mode functions (IMFs). By calculating the intra-wave frequency modulation of IMFs, that is, the difference between instantaneous and mean frequencies and amplitudes, the non-linearity of the dynamic response is quantified. Influences of wheel polygonalization on the non-linearity of steady-state and unsteady vibration responses of vehicle system are analyzed in detail. An objective criterion for wheel polygonalization health monitoring based on Degree of Non-linearity is proposed, which provides an effective tool for prognostics and health management of trains.

In order to predict the erosion life and erosion failure time of the metal mesh, based on the rotating erosion experiments under different working conditions, the erosion mass loss of the metal mesh under different factors was measured by the weight loss method and the erosion mathematical prediction model was put forward. The erosion wear mechanism of the single-layer metal mesh was explored by using electron microscope scanner and optical microscope. The results show that the erosion rate increases exponentially with the increase of liquid velocity (0.5, 1.0, 1.5, 2.0 m/s). When the solid mass fraction (0.3%, 0.5%, 0.8%) and erosion Angle is 15°–45°, the erosion rate is proportional to the solid mass fraction and erosion Angle. After erosion, the mesh samples suffered different degree of pitting corrosion, ploughing and cutting wear, and the mesh wear was local wear. Compared with the experimental value, the error of the erosion mathematical model is less than 20%, which has a certain reliability, and has an important reference significance for guiding the production control of sand oil Wells.

Most of the 3D reconstruction requirements of microscopic scenes exist in industrial detection, and this scene requires real-time object reconstruction and can get object surface information quickly. However, this demand is challenging to obtain for micro scenarios. The reason is that the microscope’s depth of field is shallow, and it is easy to blur the image because the object’s surface is not in the focus plane. Under the video microscope, the images taken frame by frame are mostly defocused images. In the process of 3D reconstruction, a single sheet or a few 2D images are used for geometric-optical calculation, and the affine transformation is used to obtain the 3D information of the object and complete the 3D reconstruction. The feature of defocus image is that its complete information needs to be restored by a whole set of single view defocus image sequences. The defocused image cannot complete the task of affine transformation due to the lack of information. Therefore, using defocus image sequence to restore 3D information has higher processing difficulty than ordinary scenes, and the real-time performance is more difficult to guarantee. In this paper, the surface reconstruction process based on point-cloud data is studied. A Delaunay triangulation method based on plane projection and synthesis algorithm is used to complete surface fitting. Finally, the 3D reconstruction experiment of the collected image sequence is completed. The experimental results show that the reconstructed surface conforms to the surface contour information of the selected object.

This article presents a model predictive control (MPC) coupled with an artificial potential field (APF) to resolve the trajectory tracking while considering the obstacle avoidance. In this article, the obstacle avoidance problem is solved by a local path planning based on the artificial potential field by constructing a virtual goal. A virtual goal is generated to produce an attractive force to guide the mobile robot to a collision-free space. The planned path is controlled by a proportional–integral–derivative (PID) controller to avoid collision. After arriving at the virtual goal, an off-line explicit MPC is calculated to obtain the optimal control inputs to track the reference trajectory. The simulation results show that the proposed method can be applied to control the mobile robot in the environment with one obstacle.

One of the challenging and important subjects in Data Envelopment Analysis (DEA) is the ranking of Decision Making Units (DMUs). In this paper, a new method for ranking the efficient DMUs is firstly proposed by utilizing the DEA technique and also developing a capable metaheuristic, imperialist competitive algorithm, derived from social, political, and cultural phenomena. Efficient DMUs are known as colonizers, and the virtual units, which are within their regions of exclusive domination, are considered as colonies. Efficient units are ranked by utilizing the factor of competition among imperialists to attract each other’s colonies. One advantage of proposed method is that, without solving any mathematical, and complex solution approaches, all extreme and non-extreme units are ranked only by comparing the pairs.

This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in white noise. The paper extends this robustness to both symmetric and asymmetric noise with outliers to achieve stronger robustness. Without the assumption of Gaussian white noise pollution, the paper employs asymmetric Laplace distribution to model broader noise, especially the asymmetrically distributed noise, since it is an asymmetric heavy-tailed distribution. Furthermore, the asymmetric Laplace (AL) distribution is represented as the product of Gaussian distribution and exponential distribution to decompose this complex AL distribution. Then, a shifted parameter is introduced as the regression term to connect the probabilistic models of the noise and the predict output that obeys shifted AL distribution. In this way, the posterior probability distribution of the unobserved variables could be deduced and the robust parameters estimation problem is solved in the general Expectation Maximization algorithm framework. To demonstrate the advantage of the proposed algorithm, a numerical simulation example is employed to identify the parameters of LPV models and to illustrate the convergence.

Many multi-agent systems (MASs) can be regarded as hybrid systems that contain continuous variables and discrete events exhibiting both continuous and discrete behavior. An MAS can accomplish complex tasks through communication, coordination, and cooperation among different agents. The complex, adaptive and dynamic characteristics of MASs can affect their stability that is critical for MAS performance. In order to analyze the stability of MASs, we propose a stability analysis method based on invariant sets and Lyapunov’s stability theory. As a typical MAS, an unmanned ground vehicle formation is used to evaluate the proposed method. We design discrete modes and control polices for the MAS composed of unmanned ground vehicles to guarantee that the agents can cooperate with each other to reliably achieve a final assignment. Meanwhile, the stability analysis is given according to the definition of MAS stability. Simulation results illustrate the feasibility and effectiveness of the proposed method.

Rotary Inverted Pendulum (RIP) mimics the behavior of many practical control systems like crane mechanism, segway, unicycle robot, traction control in vehicles, rocket stabilization, and launching. RIP is a fourth-order nonlinear open-loop unstable dynamical system and is widely used for testing the effectiveness of the newly developed control algorithms. In this paper, a Hybrid Control Scheme (HCS) based on energy balance and fuzzy logic controllers is proposed to implement the swing up and stabilization control of RIP. In the proposed control scheme, the fuzzy logic-based state feedback gains are dynamically tuned in real-time by minimizing the absolute error between the desired and actual states to get robust control performance. The proposed HCS is also compared with the conventional Linear Quadratic Controller (LQR) for this application. The comparative results show that the proposed fuzzy logic-based hybrid control scheme gives the optimal control performance in terms of achieving satisfactory transient, steady-state, and robust responses from a given RIP system, as compared to the conventional LQR based control scheme. The proposed control scheme is also relatively less complex with a low computational cost and provides desired response characteristics as compared to the existing ones in the literature.

This paper focuses on the time-varying output formation (TVOF) tracking control of heterogeneous linear multi-agent systems (HL-MASs) with both delays and switching topologies, where the followers’ outputs can move along the reference trajectory generated by the leaders and maintain the desired time-varying formation. First, a distributed observer is proposed for each follower, aiming to estimate the convex combination of leaders’ state with both communication delays and switching graphs. The observer’s error for heterogeneous MASs is analyzed based on Lyapunov theory and linear matrix inequality (LMI) technique. Second, the observer is incorporated into the output formation tracking protocol. Then, an algorithm is put forward to calculate the control feedback gains and the formation tracking feasibility constraint is also provided. Furthermore, the convergence of the formation tracking error is proved. At last, the effectiveness of this proposed method is validated through a numerical simulation.