A boundary control approach is used to control a two-link rigid-flexible wing in this article. Its design is based on the principle of bionics to improve the mobility and the flexibility of aircraft. First, a series of partial differential equations (PDEs) and ordinary differential equations (ODEs) are derived through the Hamilton's principle. These PDEs and ODEs describe the governing equations and the boundary conditions of the system, respectively. Then, a control strategy is developed to achieve the objectives including restraining the vibrations in bending and twisting deflections of the flexible link of the wing and achieving the desired angular position of the wing. By using Lyapunov's direct method, the wing system is proven to be stable. The numerical simulations are carried out with the finite difference method to prove the effectiveness of designed boundary controllers.

Dynamical Modeling and Boundary Vibration Control of a Rigid-Flexible Wing System

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

In this article, an admittance-based controller for physical human–robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners —This work is motivated by the neglect of safety in existing controller design in physical human–robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human–robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: a differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: a learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector.

Admittance-Based Controller Design for Physical Human–Robot Interaction in the Constrained Task Space

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor–critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov’s direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control.

Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation

In this paper, we address simultaneous control of a flexible spacecraft’s attitude and vibrations in a three-dimensional space under input disturbances and unknown actuator failures. Using Hamilton’s principle, the system dynamics is modeled as an infinite dimensional system captured using partial differential equations. Moreover, a novel adaptive fault tolerant control strategy is developed to suppress the vibrations of the flexible panel in the course of the attitude stabilization. To determine whether the system energies, angular velocities and transverse deflections, remain bounded and asymptotically decay to zero in the case wherein the number of actuator failures is infinite, a Lyapunov-based stability analysis is conducted. Finally, extensive numerical simulations are performed to demonstrate the performance of the proposed adaptive control strategy.

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion; the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation (NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNN-LSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.

Motion trajectory prediction based on a CNN-LSTM sequential model

The invention discloses a train communication system and method based on AP (Assess Point) switching The train communication system comprises a central layer, a trackside layer and a vehicle-mounted layer which are in wireless communication in sequence, wherein the central layer comprises a master wireless controller and an auxiliary wireless controller respectively connected with a server through a backbone network, the trackside layer comprises two or more APs linearly distributed beside a track, and the vehicle-mounted layer communicates with the APs through an STA (single thread apartment) As for the rain communication system and method based on AP switching, firstly, the architecture of a CBTC (communication based train control) system is improved firstly, the wireless controller is introduced on the central control layer of a train, the functional configuration of APs is simplified so that other functional configurations of the APs are transferred upwards and the functions are intensively realized in the wireless controllers; and secondly, a communication channel for the APs and the STA is designed, and the STA can work without searching an appropriate communication channel in the detection stage

Train communication system and method based on AP (Assess Point) switching

As a powerful and object-oriented graphical modeling language, Unified Modeling Language (UML) has been introduced into the design and development of safety-critical computer systems. However, UML model is difficult to reflect the concurrency and consistency of constraint rules between objects and impossible to completely demonstrate the dynamic behavior characteristics of a system. Further, it cannot be directly expressed and analyzed by mathematical tools. Petri net modeling technology uses graphical element place and transition to clearly describe the internal interactions of a system. Meanwhile, Petri net has mature mathematical analysis methods. We have proposed to combine these two tools for designing and analyzing a system with a set of transformation rules from UML statecharts to Petri net. In this paper, an automatic conversion tool is introduced. The tool performs the transformation with three steps as follows: (1) Transforming UML model into XML document, which makes information extraction of the UML model become easier. (2) Analyzing semantics of UML model and Petri net model, then designing data structure for transformation, and completing the transformation based on the transformation rules. (3) Analyzing the transformed Petri net model to verify the characteristics of the UML model. The transformation rules and the tools are developed based on a new railway interlocking system.

Automatic transformation from UML statechart to Petri nets for safety analysis and verification

The invention discloses a train air conditioning unit fault diagnosing method based on a multi-classification strategy. The diagnosing method comprises the steps of firstly calculating the distribution densities of all categories according to actual normal operation data and various fault operation data of a train air conditioning unit; then ranking based on the junction distribution densities; and finally utilizing the ranking results to establish a binary classification strategy tree based on a PSVM, learning and training to obtain a fault diagnosing model of the train air conditioning unit and utilizing the fault diagnosing model of the train air conditioning unit to diagnose and determine fault types of the train air conditioning unit. The train air conditioning unit fault diagnosing method based on the multi-classification strategy can effectively improve the fault diagnosing accuracy.

Train air conditioning unit fault diagnosing method based on multi-classification strategy

The axle temperature of the high speed train is the most direct reflection of the train operating conditions while it is also affected by many factors. The factors which significantly affect the axle temperature are screened out by using the stepwise regression analysis and the prediction equation of the axle temperature is established, so as to compare the predicted data and the measured data. The validity of the coefficients in the equation is verified through R-squared, F test and T test. The experiment shows that R-squared is between 0.81 and 0.93, indicating a high degree of fitting prediction equations, and F test results show that the overall equation is significantly better. The results of T test indicate that the velocity, the carrying capability and the ambient temperature have significant influence on the change of axle temperature. But the traction and the power of traction have less effect. The result shows that this method can reflects the variation trend of axle temperature, which can provide support to the operation and maintenance of axle.

Ma Weigang

Papers

Motion trajectory prediction based on a CNN-LSTM sequential model

Train communication system and method based on AP (Assess Point) switching

Automatic transformation from UML statechart to Petri nets for safety analysis and verification

Train air conditioning unit fault diagnosing method based on multi-classification strategy

A Prediction Method Based on Stepwise Regression Analysis for Train Axle Temperature