Shenyang Institute of Automation,Chinese Academy Of Sciences
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Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm
No full textThe stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.</p
Study on the Effect of Loading Method on Mechanical Behavior of Rockbolt Based on DEM Modeling
No full textFully grouted rockbolt is a form of reinforcement commonly used in underground projects. Its mechanical behavior is of vital importance to enhancing the stability of surrounding rock masses. To investigate the effect of different loading methods on the mechanical behaviors of rockbolt, the distinct element code PFC2D was adopted to simulate the pullout test on rockbolt and a compression test on the bolted rock mass, with Hongtoushan Copper Mine as the engineering background. The stress distribution along rockbolt and the maximum pullout capacity were analyzed. The results indicated that the loading method has significant effects on the mechanical behaviors of rockbolt. In the pullout test, the peak stress in the rockbolt always occurs near the free face. As the rock mass is a passive load-bearing component, the cracks and weak intercalations in the rock mass have little effect on the mechanical behaviors of the rockbolt. Meanwhile, as the external load acts directly on the outer end of rockbolt, the effect of the bearing plate cannot be reflected. However, in the compression test on the bolted rock mass, the peak stress in the rockbolt gradually transfers to greater depth. The existence of cracks and weak intercalations have an impact on the mechanical behaviors of the rockbolt. In addition, with increasing size of the bearing plate, the maximum pullout capacity of the rockbolt gradually increases. Comparison of the simulation results showed that the maximum pullout capacity obtained by the pullout test on a rockbolt is higher than that obtained by the compression test on a bolted rock mass. Therefore, appropriate reduction of the maximum pullout capacity obtained by the pullout test is suggested for rockbolt design, and the reduction factor should be in the range of 0.7-0.8.</p
An improved minimal error model for the robotic kinematic calibration based on the POE formula
No full textThe conventional product of exponentials (POE)-based methods dissatisfy the parametric minimality for the kinematic calibration of serial robots due to overlooking the magnitude and pitch constraints. Thus, the minimal kinematic model is presented to solve this problem, which can be developed further. This paper puts forward an improved algorithm for the minimal parameter calibration. An actual kinematic model with the minimal parameters (MP) is constructed according to the geometric properties of actual joint twists in the auxiliary frames established on the basis of the nominal joint axes. Then, the initial pose error is defined in the tool coordinate frame, which is expressed as the exponential map of the twist, and all twist descriptions are unified, so as to give a unified kinematic model in mathematics. By differentiating the kinematic model, a minimal error model is derived in explicit form. Subsequently, we propose a novel parameter identification method, which identifies the orientation error and position error parameters separately by the iterative least-squares method and updates the MP uniformly. Finally, the simulations and experiments on the different serial robots are conducted to verify the correctness and effectiveness of the proposed algorithm. The simulation results show our calibration algorithm outperforms the existing ones in the accuracy aspect, and the experiment result shows that the absolute pose accuracy of the UR5 industrial robot is upgraded about 9 times under a statistics sense after the calibration
Event-Triggered Cooperative Output Regulation of Heterogeneous Multi-Agent Systems with Adaptive Fault-Tolerant Control
No full textThis paper studies the cooperative output regulation problem for heterogeneous multi-agent systems with actuator faults. A new two-layer distributed control strategy is proposed: (i) Two types of distributed observers are designed for agents to estimate the exogenous signal, and an adaptive event-triggering mechanism is introduced to reduce unnecessary information transmission between agents; (ii) A decentralized adaptive fault-tolerant control scheme is proposed to achieve the cooperative output regulation of heterogeneous multi-agent systems and compensate for the actuator faults automatically. Finally, a numerical example is given to verify the feasibility of the proposed algorithm. IEEE</p
Fault detection and isolation of actuator failures in jet engines using adaptive dynamic programming
No full textThis paper presents a adaptive dynamic programming-based fault detection and isolation (FDI) scheme to detect and isolate faults in an aircraft jet engine. To this end, the weights in Actor-Critic neural networks are first tuned to learn the input-output map of the jet engine considering its multiple working modes. The convergences of the trainings in Critic-Actor neural networks are strictly proved without knowing the drift dynamics and the input dynamics in the presence of unknown nonlinearities and approximation errors. Using the residuals that are generated by measuring the difference of each network output and the measured engine output, various criteria are established for accomplishing the fault diagnosis task, that addresses the problem of fault detection and isolation of the system components. A number of simulation studies are carried out for combustion chamber of a single-spool jet engine to demonstrate and illustrate the advantages, capabilities, and performance of our proposed fault diagnosis scheme.</p
Non-local channel aggregation network for single image rain removal
No full textRain streaks showing in images or videos would severely degrade the performance of computer vision applications. Thus, it is of vital importance to remove rain streaks and facilitate our vision systems. While recent convolutional neural network based methods have shown promising results in single image rain removal (SIRR), they fail to effectively capture long-range location dependencies or aggregate convolutional channel information simultaneously. However, as SIRR is a highly ill-posed problem, these spatial and channel information are very important clues to solve SIRR. First, spatial information could help our model to understand the image context by gathering long-range dependency location information hidden in the image. Second, aggregating channels could help our model to concentrate on channels more related to image background instead of rain streaks. In this paper, we propose a non-local channel aggregation network (NCANet) to address the SIRR problem. NCANet models 2D rainy images as sequences of vectors in three directions, namely vertical direction, transverse direction, and channel direction. Recurrently aggregating information from all three directions enables our model to capture the long-range dependencies in both channels and spatial locations. Extensive experiments on both heavy and light rain image data sets demonstrate the effectiveness of the proposed NCANet model.</p
Codesign of Architecture, Control and Scheduling of Modular Cyber-Physical Production Systems for Design Space Exploration
No full textDesign space exploration (DSE) of cyber-physical production systems (CPPS) is a search problem in the space of potential compositional configurations. Current design methodologies follow the separated design paradigm in which the architecture, control, and scheduling are separately designed. Optimization of each part considers only the corresponding goals of interest and overlooks other aspects by adopting gross assumptions, which makes it difficult to determine the global optimal solution for a given system. To address this problem, we propose a codesign method that considers the design spaces of architecture, control and scheduling as monolithic, mixed discrete-continuous spaces. We formulate DSE as an optimization problem and propose a generic iterative algorithm scheme involving simulation in-the-loop to solve the above new problem. To illustrate the effectiveness of the proposed method, a real single-stage reducer CPPS is considered. The design results demonstrate that our method provides better solutions than does the separated design method.</p
Sensor fault estimation and fault tolerant control for IT2 fuzzy system via sliding mode approach
No full textThe point of this article is the sensor fault estimation and fault-tolerant controller design for IT2 fuzzy systems via sliding mode approach. In order to estimate accurately the system states and sensor faults, a novel proportional and derivative sliding mode observer is introduced, which provide more design freedom and eliminate the effects of sensor faults. By splitting the operating domain and estimating the membership functions, a set of relaxation stability conditions subject to the information of membership functions and system states are obtained. Then, a sliding mode controller in form of IT2 fuzzy model is designed to stabilize the closed-loop systems. An example of bolt-tightening tool model is considered to demonstrate the validity of the proposed results.</p
Non-local channel aggregation network for single image rain removal
No full textRain streaks showing in images or videos would severely degrade the performance of computer vision applications. Thus, it is of vital importance to remove rain streaks and facilitate our vision systems. While recent convolutional neural network based methods have shown promising results in single image rain removal (SIRR), they fail to effectively capture long-range location dependencies or aggregate convolutional channel information simultaneously. However, as SIRR is a highly ill-posed problem, these spatial and channel information are very important clues to solve SIRR. First, spatial information could help our model to understand the image context by gathering long-range dependency location information hidden in the image. Second, aggregating channels could help our model to concentrate on channels more related to image background instead of rain streaks. In this paper, we propose a non-local channel aggregation network (NCANet) to address the SIRR problem. NCANet models 2D rainy images as sequences of vectors in three directions, namely vertical direction, transverse direction, and channel direction. Recurrently aggregating information from all three directions enables our model to capture the longrange dependencies in both channels and spatial locations. Extensive experiments on both heavy and light rain image data sets demonstrate the effectiveness of the proposed NCANet model. (c) 2021 Elsevier B.V. All rights reserved
Representative Task Self-Selection for Flexible Clustered Lifelong Learning
No full textConsider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are of prescribed size and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our FCL model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then: 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multitask data sets, and the experimental results demonstrate that our FCL model can achieve better performance than most lifelong learning frameworks, even batch clustered multitask learning models.</p