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Incremental Stability of Discrete-Time First-Order Projection Elements ⁎

IFAC-PapersOnLine 2024年第11期58卷 123-128页

作者： Riccardo Bertollo Sebastiaan van den Eijnden W.P.M.H. (Maurice) Heemels Control Systems Technology section Department of Mechanical Engineering Eindhoven University of Technology The Netherlands

In this paper, we present and analyze a class of discrete-time and sampled-data controllers named first-order projection elements (FOPEs). FOPEs form a class of hybrid controllers that have an underlying linear base dynamics, which are combined with a projection operator that ensures that the input-output (I/O) pair is contained in a well-designed sector. The I/O sector-constraint enables performance benefits in various application domains including motion control of high-precision machines. After the introduction of FOPEs, we focus on incremental stability analysis of these hybrid controllers. We show that the properties of the projection operator ensure incremental stability of the open-loop FOPE dynamics in case of an underlying linear dynamics that is exponentially stable. In this case, we compute tight bounds on the incremental gain. Under an appropriate small-gain condition, we show incremental input-to-state stability of the sampled-data interconnection of a FOPE with a continuous-time linear time-invariant plant. For the case of projected integrator dynamics, we provide a counterexample showing that incremental stability can be compromised. We provide additional, necessary and sufficient conditions under which FOPEs with integrator dynamics are incrementally asymptotically stable. Numerical simulations provide a practical example of the established incremental properties for the trajectories of a dynamical system coming from an industrial motion control application.

关键词： Hybrid integrator-gain systems Projected dynamics Incremental stability Sampled-data systems

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A Tailsitter UAV with a Passive Joint Able to Turn Acutely with a Flip Motion

A Tailsitter UAV with a Passive Joint Able to Turn Acutely w...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Takumi Ito Riku Funada Mitsuji Sampei Department of Systems and Control Engineering Tokyo Institute of Technology

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

This paper proposes a novel hybrid UAV that can turn acutely with flip-turn motion. The UAV has a link consisting of four rotors, named quadlink, which is connected to the fuselage via a passive joint. When the UAV wants to turn in the reverse direction, only the quadlink rotates to generate thrust in the reverse direction for deceleration without changing the attitude of the wings. This eliminates the vertical motion during the deceleration of the conventional tailsitter UAVs and achieves acute turning. Then, once the UAV sufficiently slows down, it accelerates in the reverse direction while flipping the fuselage upside down to point the nose in the reverse direction. Finally, the UAV fully transitions to reverse cruise flight in the opposite direction. This paper only considers longitudinal motion as the first step in controlling the proposed UAV. The acute turn is accomplished without switching controllers. The paper finally presents a simulation result to evaluate the performance of the proposed UAV and controller.

关键词： Computer aided software engineering Simulation Rotors Nose Switches Autonomous aerial vehicles Turning control systems Mathematical models Trajectory

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Design and control of a VTOL Aerial Vehicle Tilting its Rotors Only with Rotor Thrusts and a Passive Joint

Design and Control of a VTOL Aerial Vehicle Tilting its Roto...

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European control Conference (ECC)

作者： Takumi Ito Riku Funada Mitsuji Sampei Department of Systems and Control Engineering Tokyo Institute of Technology

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel VTOL UAV that owns a link connecting four rotors and a fuselage by a passive joint, allowing the control of the rotor's tilting angle by adjusting only the rotors' thrust. This unique structure contributes to eliminating additional actuators, such as servo motors, to control the tilting angles of rotors, resulting in the UAV's weight lighter and simpler structure. We first derive the dynamical model of the newly designed UAV and analyze its controllability. Then, we design the controller that leverages the tiltable link with four rotors to accelerate the UAV while suppressing a deviation of the UAV's angle of attack from the desired value to restrain the change of the aerodynamic force. Finally, the validity of the proposed control strategy is evaluated in simulation study.

关键词： Analytical models Force Rotors Europe Autonomous aerial vehicles controllability Aerodynamics

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Counting assistant through multi-sensor fusion for inventory monitoring 27

Counting assistant through multi-sensor fusion for inventory...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Lin, Ruofan Tiwari, Ashutosh Oyekan, John The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

A large number of parts are used in the production activities of the assembly plant. The quantity and quality of these parts in the process of transportation, distribution, use, and recycling in the factory have an impact on production. With large amount of parts and tools, how to provide the right types and quantity of parts at the time and right location becomes the key to business success. The traditional method uses manpower which is costly and time-delayed. Previous single sensor monitoring has its own limitations. For example, weight recognition is hard to determine when multiple type items are mixed. Visual identification is susceptible to problems such as occlusion and difficulty in identifying dense areas and every item needs to be tagged by using RFID. Therefore, this paper introduces the multi-sensor method, which uses a mathematical approach to fuse weight and visual data to predict different types and quantities of parts in small containers. This study also provides a case study, simulated classification and counting of Aircraft General Standard(AGS) parts, which are mainly fasteners. They are small in size, expensive, and difficult to identify by a single sensor. Fusion results show up to 80% in average accuracy which is an improvement over single vision(73%) and weight detection up to 50%. © 2022 IEEE.

关键词： Aircraft

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Output Tracking for Uncertain Time-Delay systems via Robust Reinforcement Learning control

Output Tracking for Uncertain Time-Delay Systems via Robust ...

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第43届中国控制会议

作者： Zezhi Tang J Anthony Rossiter Xin Jin Benyuan Zhang George Panoutsos Department of Automatic Control and Systems Engineering University of Sheffield

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The composite optimal output tracking control problem of time-delay uncertain systems is investigated in this paper. To improve the robustness of the uncertain systems, a disturbance observer-based(DOB) compensation scheme is proposed to reduce the influence of the *** further enhance the control efficiency, we incorporate a Smith predictor into the control framework to precisely predict future states of the delayed system. Subsequently, a composite actor-critic reinforcement learning(RL) scheme is built for approximating both the ideal value function associated with the compensated system and the optimal control policy in real-time, by augmenting the system states with the output tracking error. To counteract the impact of the probing signal, concurrent learning is employed in this paper with the data collected from the known system *** results based on an experimental platform of slot die coating machines demonstrate the effectiveness of the proposed scheme.

关键词： Smith Output Tracking for Uncertain Time-Delay systems via Robust Reinforcement Learning control

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Tide Level Prediction Using NARX-based Recurrent Neural networks 27

Tide Level Prediction Using NARX-based Recurrent Neural Netw...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Nikentari, Nerfita Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Tide level forecasting is one of major components in oceanography and marine research. The future prediction of tides is an important issue for human and environment. This study proposes new tide level prediction methods with multiple input multiple output (MIMO) approach based on nonlinear autoregressive with exogenous inputs (NARX) model and three types of recurrent neural networks (RNNs), namely, long short-term memory (LSTM), bi-directional long short-term memory (biLSTM) and gated recurrent unit (GRU) networks. We develop three types of NARX models: NARX-LSTM, NARX-biLSTM and NARX-GRU. We evaluate the three models and compare their performances with LSTM, biLSTM and GRU network models. It turns out that the proposed MIMO NARX based RNN models, especially NARX-LSTM and NARX-GRU have better performance in comparison with their RNN counterparts (i.e., LSTM, biLSTM, and GRU). The NARX-GRU model shows the best performance for one step and three step ahead prediction in terms of RMSE in each location and average RMSE, while NARX-LSTM performs better for two steps ahead prediction in terms average RMSE. The results also show that the mixture of RNN and NARX improves the performance of RNN model for all the four tasks at four locations in the UK (Wick, Workington, Ullapol and New Heaven) and for the three time horizons (i.e., 1, 2 and 3 steps ahead). © 2022 IEEE.

关键词： Forecasting

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A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and Classification 27

A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and C...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Wang, Guoliang Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Rolling bearings play a significant role in the operation of rotating machinery. Finding bearing faults in the early stage can not only keep machinery running safely but also avoid economic loss. Traditional machine learning (ML) and deep learning (DL) are two important classes of fault diagnosis methods. The process of feature extraction is significant for ML based methods and many signal processing techniques have been used in this process. However, when dealing with massive and complex signals, features extracted by these techniques may not fully describe the characteristics of faults. Convolutional neural network (CNN) models are applied in many areas, including image identification and classification, pattern recognition, and speech processing. In this paper, a novel hybrid model by combining one dimensional CNN (1DCNN) and support vector machine (SVM) is proposed for bearing fault diagnosis and classification, which is called 1DCNN-SVM model or method. The first part of the model, 1DCNN, is used to extract features of raw data, whereas the second part, SVM, is employed to carry out the fault recognition and classification tasks. The evaluation of the proposed method is operated based on a real dataset. The experiment results show that the fault diagnosis accuracy of proposed method based on the hybrid model is highly improved compared to the traditional SVM based methods. © 2022 IEEE.

关键词： Failure analysis

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Machine Learning for Medical and Healthcare Data Analysis and Modelling: Case Studies and Performance Comparisons of Different Methods 27

Machine Learning for Medical and Healthcare Data Analysis an...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Sun, Bo Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

In smart healthcare, binary classification is one of the most important tasks for disease or clinical outcome prediction. Machine learning (ML) methods have great potential to discover knowledge from data. While there are a wide range of ML methods that are potentially useful, the selection, design and implementation of an appropriate method from a variety of ML models for specific prediction tasks is still quite challenging in many medical and healthcare data modelling tasks. This paper investigates, evaluate and compares nine typical representative ML methods using four clinical and healthcare datasets. The main attention is paid to the strengths and weaknesses of these methods and provide suggestions to researchers in related fields. Results demonstrate that ensemble learning (such as Adaboost and random forest) outperform the other approaches in prediction accuracy and interpretability. © 2022 IEEE.

关键词： Adaptive boosting

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Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels 16

Benchmarking Semantic Communications for Image Transmission ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong University China School of Computer Science and Technology Qilu University of Technology China Eurecom Communication Systems Department France

ISBN: (纸本)9798350390643

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes. © 2024 IEEE.

关键词： Radio transceivers

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Multi-Task Learning for Time Series Forecasting Using NARMAX-LSTM 27

Multi-Task Learning for Time Series Forecasting Using NARMAX...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Nikentari, Nerfita Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Traditional time series forecasting models based on neural networks are usually designed for a single task, which ignores the source of knowledge or information from other related tasks. On the other hand, multitask learning (MTL) offers a new perspective where one model is not only for one forecasting case or task but several tasks at the same time. The MTL model proposed in this study is based on the Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) model and Long-Short Term Memory (LSTM) Neural networks. The NARMAX-LSTM model is tested and validated using three different time horizons and different cases/tasks. The model forecasting performance is compared with that of three other linear and nonlinear models. The comparison results show that the multi-task NARMAX-LSTM model does not only have better prediction performance but also avoid suffering from negative transfer and overfitting. © 2022 IEEE.

关键词： Forecasting

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