생체의 모델링과 Simulation

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model’s loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

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Basic Biomechanics of the Musculoskeletal System

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2016.0208

LSTM network: a deep learning approach for short-term traffic forecast

This study addresses the problem of trajectory control of a flexible two-link manipulator on the basis of the partial differential equation (PDE) dynamic model. One of the key contributions of this study is that a novel non-linear PDE observer is proposed to estimate distributed positions and velocities along flexible links, which cannot be achieved by the typical ordinary differential equation observer. In addition, the rigidity-flexibility coupling dynamics is decomposed using the singular perturbation approach, thus providing convenience for control design. Based on the proposed observer and the decoupled PDE model, a boundary control scheme is designed to regulate the end effector along reference trajectory in task space and suppress vibration simultaneously. The asymptotic stability of both the proposed observer and the control algorithm is validated by theoretical analysis and demonstrated by simulation results, respectively.

Observer-based partial differential equation boundary control for a flexible two-link manipulator in task space

This paper presents a robust adaptive iterative learning control (ILC) algorithm for 3-DOF permanent magnet (PM) spherical actuators to improve their trajectory tracking performance. The dynamic model of a PM spherical actuator is a multivariable nonlinear system with interaxis coupling terms. Uncertainties such as modeling errors, loads, and external disturbances exist in the model unavoidably, which will affect the performance, including the precision of the control system. Hence, to compensate for these uncertainties, a new hybrid control scheme that consists of a proportional–derivative (PD) feedback control with varying gains, a PD-type ILC with adjustable gains, and a robust term is developed. The new control law combines the advantages of simplicity and easy design of the PD control, the effectiveness of the ILC to handle model uncertainties and repetitive disturbances, and the robustness of the robust term to random disturbances. In addition, to expedite the convergence rate, the gains in the PD feedback control and the PD-type ILC are adaptively adjusted according to the iteration times. It is shown that the system tracking error approaches zero as the number of iterations increases. To demonstrate the proposed algorithm and verify the established theoretical results, both simulations and experiments are conducted. The results have shown that the proposed control algorithm can effectively compensate for various uncertainties and can thus improve the trajectory tracking performance of spherical actuators.

A Robust Adaptive Iterative Learning Control for Trajectory Tracking of Permanent-Magnet Spherical Actuator

This paper presents a permanent magnet (PM) spherical actuator embedded with a novel three-dimensional (3D) orientation measurement system. The study covers actuator design, torque modeling, and motion control. The spherical actuator consists of a stator with 24 coils and a rotor with 8 PM poles. A high accuracy and resolution, non-blinding 3D measurement mechanism is designed for the rotor orientation detection. The torque output is formulated from finite element (FE) computation and curve fitting method. Due to the nonlinear property of dynamic model, computed torque control law is utilized for the three degree-of-freedom (3-DOF) system motion control. The space geometry method is employed to study the redundancy feature of current inputs, which offers the opportunity for control optimization to improve the power efficiency and the fault tolerance capability of the spherical actuator. Simulations and experiments are then conducted on the developed prototype to validate the design concept, mathematic models and control strategy.

Design and control of a three degree-of-freedom permanent magnet spherical actuator

In this study, an output feedback-based adaptive neural controller is presented for a class of uncertain non-affine pure-feedback non-linear systems with unmodelled dynamics. Two major technical difficulties for this class of systems lie in: (i) the few choices of mathematical tools in handling the non-affine appearance of control in the systems, and (ii) the unknown control direction embedded in the unknown control gain functions, in great contrast to the standard assumptions of constants or bounded time-varying coefficients. By exploring the new properties of Nussbaum gain functions, stable adaptive neural network control is possible for this class of systems by using a strictly positive-realness-based filter design. The closed-loop system is proven to be semi-globally uniformly ultimately bounded, and the regulation error converges to a small neighbourhood of the origin. The effectiveness of the proposed design is verified by simulations.

Jingmeng Liu

Papers

LSTM network: a deep learning approach for short-term traffic forecast

Observer-based partial differential equation boundary control for a flexible two-link manipulator in task space

A Robust Adaptive Iterative Learning Control for Trajectory Tracking of Permanent-Magnet Spherical Actuator

Design and control of a three degree-of-freedom permanent magnet spherical actuator

Adaptive neural network output feedback control for a class of non-affine non-linear systems with unmodelled dynamics