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arXiv 2020年

作者： Rösmann, Christoph Makarow, Artemi Bertram, Torsten Institute of Control Theory and Systems Engineering TU Dortmund University D-44227 Germany

This paper deals with the development and analysis of novel time-optimal point-to-point model predictive control concepts for nonlinear systems. Recent approaches in the literature apply a time transformation, however, which do not maintain recursive feasibility for piecewise constant control parameterization. The key idea in this paper is to introduce uniform grids with variable discretization. A shrinking-horizon grid adaptation scheme ensures convergence to a specific region around the target state and recursive feasibility. The size of the region is configurable by design parameters. This facilitates the systematic dual-mode design for quasi-time-optimal control to restore asymptotic stability and establish a smooth stabilization. Two nonlinear program formulations with different sparsity patterns are introduced to realize and implement the underlying optimal control problem. For a class of numerical integration schemes, even nominal asymptotic stability and true time-optimality are achieved without dual-mode. A comparative analysis as well as experimental results demonstrate the effectiveness of the proposed techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Asymptotic stability

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Time-optimal control with direct collocation and variable discretization

arXiv

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arXiv 2020年

作者： Rösmann, Christoph Makarow, Artemi Bertram, Torsten Institute of Control Theory and Systems Engineering TU Dortmund University Dortmund44227 Germany

— This paper deals with time-optimal control of nonlinear continuous-time systems based on direct collocation. The underlying discretization grid is variable in time, as the time intervals are subject to optimization. This technique differs from approaches that are usually based on a time transformation. Hermite-Simpson collocation is selected as common representative in the field of optimal control and trajectory optimization. Hereby, quadratic splines approximate the system dynamics. Several splines of different order are suitable for the control parameterization. A comparative analysis reveals that increasing the degrees of freedom in control, e.g. quadratic splines, is not suitable for time-optimal control problems due to constraint violation and inherent oscillations. However, choosing constant or linear control splines points out to be very effective. A major advantage is that the implicit solution of the system dynamics is suited for stiff systems and often requires smaller grid sizes in practice. Copyright © 2020, The Authors. All rights reserved.

关键词： System theory

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Study on a Two-layer Path Planning Method of Spherical Multi-robot

Study on a Two-layer Path Planning Method of Spherical Multi...

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IEEE International Conference on Mechatronics and Automation

作者： Chenqi Li Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated systems and Intelligent Robot Laboratory Tianjin University of Technology BinshuiXidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering Systems and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

A path planning method based on a two-layer coordination system is proposed for the compatibility requirement of path planning global optimum and local coordination in a spherical multi-robot system. In the first layer of the algorithm, the global path quality is improved by proposing a nonlinear sine factor and a sparrow search algorithm with improved levy flight strategy to plan the global collision-free optimal path for each robot. In the second layer, a rolling window method is used to determine the possible collision positions of each robot in the system, and a priority-based conflict resolution strategy is proposed to complete the local coordination of the path near the corresponding position. The experimental results show that the path planning method enables the robots to drive along the global optimal path, but still can carry out flexible and orderly local path coordination, which effectively improves the path planning performance of the system.

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Non-intrusive Time-POD for Optimal control of a Fixed-Bed Reactor for CO 2 Methanation

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IFAC-PapersOnLine 2021年第3期54卷 122-127页

作者： Jens Bremer Jan Heiland Peter Benner Kai Sundmacher Max Planck Institute Magdeburg Dpt. Process Systems Engineering Sandtorstraße 2 39106 Magdeburg Germany and Otto von Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg (Sundmacher) Max Planck Institute Magdeburg Dpt. Computational Methods in Systems and Control Theory and Otto von Guericke University Magdeburg Fakultät für Mathematik

The optimization of a controlled process in a simulation without access to the model itself is a common scenario and very relevant to many chemical engineering applications. A general approach is to apply a black-box optimization algorithm to a parameterized control scheme. The success then depends on the quality of the parametrization that should be low-dimensional though rich enough to express the salient features. This work proposes using solution snapshots to extract dominant modes of the temporal dynamics of a process and use them for low-dimensional parametrizations of control functions. The approach is called proper orthogonal decomposition in time (time-POD). We provide theoretical reasoning and illustrate the performance for the optimal control of a methanation reactor.

关键词： Optimal control Model order reduction Proper orthogonal decomposition Fixed-bed reactor Carbon dioxide methanation Nonlinear programming Black-box model

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Prediction Model of User Purchase Behavior Based on Machine Learning 17

Prediction Model of User Purchase Behavior Based on Machine ...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Zhai, Xiang Shi, Peng Xu, Liang Wang, Yalong Chen, Xi Tianjin University of Technology Binshui Xidao 391 Tianjin Key Laboratory for Control Theory Applications in Complicated Systems School of Electrical and Electronic Engineering Tianjin China Luoyang Institute of Electro-Optical Equipment Avic Luoyang471000 China

ISBN: (纸本)9781728164151

In recent years, with the continuous popularity of the Internet, the number of online shopping users in my country has reached 639 million, which contains huge commercial value. In order to maintain the prosperity, diversity and order of merchants, and fully meet consumers' one-stop shopping needs, it is necessary to analyze and predict user purchase behaviors more accurately. This article uses the interaction between users and products within a certain period of time provided by *** to predict whether users will place orders in designated categories and shops in the next week. First of all, we aim at the problem that the original features are not strongly related, and use feature fusion to construct strong features. For high-dimensional systems that are slow to calculate and prone to overfitting, we use random forests to filter features, reducing the complexity of the model. Finally, a fusion algorithm based on XGBoost and LightGBM is proposed to predict the purchase behavior of users, and the prediction effects of random forest and GBDT algorithm are compared at the same time. The results show that the fusion model has the best prediction effect, and can help merchants more accurately conduct marketing activities to potential customers based on the prediction results. © 2020 IEEE.

关键词： Forecasting

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Liu, Yu Zhang, Yuexin Li, Kunming Qiao, Yongliang Worrall, Stewart Li, You-Fu Kong, He Shenzhen518055 China Department of Mechanical Engineering City University of Hong Kong Hong Kong School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Australian Centre for Field Robotics The University of Sydney NSW2006 Australia Australian Institute for Machine Learning The University of Adelaide SA5005 Australia Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering crossdataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme. © 2024, CC BY.

关键词： Trajectories

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Accelerating Explicit Model Predictive control by Constraint Sorting ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11356-11361页

作者： Juraj Holaza Juraj Oravec Michal Kvasnica Raphael Dyrska Martin Mönnigmann Miroslav Fikar Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Slovakia Automatic Control and Systems Theory Ruhr-Universität Bochum Germany

Explicit MPC represents one of the fastest ways of real-time MPC implementation. As the explicit MPC policy is optimization-free in real-time control, its efficiency is determined by solving a point location problem. This paper proposes the novel concept of accelerating explicit MPC that significantly speeds up the real-time evaluation of the point location problem. The introduced strategy has two layers: (i) an offline phase determines a smart order of the regions to be explored, and (ii) an online phase removes further regions to be explored on the fly based on the current value of the value function. The main advantage of layer (i) is that the order is evaluated offline, therefore, it does not increase the real-time implementation of explicit MPC. The implementation of layer (ii) slightly increases the real-time evaluation but leads to further speed-up of the point location problem. As the proposed layers are based just on the evaluation of some appropriate value function, the main benefit is that these layers are fully applicable also for higher-dimensional systems. Although the accelerated explicit MPC variant does not reduce the worst-case time of solving the point location problem, an extensive case study demonstrates the efficiency of the proposed strategy.

关键词： Explicit model predictive control point location problem sequential search

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PID control for Aeroengine Based on Sparrow Search Algorithm

PID Control for Aeroengine Based on Sparrow Search Algorithm

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Chinese Automation Congress (CAC)

作者： Liang Xu Hao Wang Ying Liu Wei Xue Ningning Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China National Key Laboratory for Complex Systems Simulation Beijing China Beijing Aerospace Propulsion Institute Beijing China

ISBN: (纸本)9781665426480

To address the problem of aeroengine PID parameter optimization and tuning, this work employs the excellent performance of the sparrow search algorithm (SSA) with high convergence accuracy and fast convergence speed to propose a self-tuning PID control method (SSA-PID) for aeroengine parameters. Considering that the oil supply output by the actuator may have a substantial overshoot, the proposed technique adds a penalty mechanism for the overshoot at the output, designs and improves the fitness function weighted by performance indicators, and effectively avoids the possible excessive overshoot and sharp oil supply phenomenon of the engine oil supply. The SSA-PID simulates and verifies the mathematical models of the turbofan engine’ s steady-state operating points on the ground and at high altitudes. The results show that the SSA-PID control method has better parameter tuning and optimization effects, a faster speed step response, and generates a fuel supply that does not have a targe overshoot, thereby proving its effectiveness and feasibility.

关键词： PI control Oils Search problems Mathematical models Steady-state PD control Fuels

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Machine Learning Assisted Solutions of Mixed Integer MPC on Embedded Platforms

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IFAC-PapersOnLine 2020年第2期53卷 5195-5200页

作者： Yannik Löhr Martin Klaučo Miroslav Fikar Martin Mönnigmann Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava

Many control applications, especially in the field of energy systems, require a simultaneous decision for continuous and binary values of control inputs. In optimal control methods like model predictive control (MPC), this leads to the problem of solving expensive mixed-integer programs online. As this solution in practice has to be calculated with low cost embedded hardware with low energy demand, it is necessary to reduce the computational demand in advance. We present an approach to replacing the mixed-integer program by a simpler quadratic program by means of learning techniques. To be more specific, we design a neural network and a support vector machine to classify the optimal control policies for the binary inputs offline and evaluate this decision in the online step as the basis for the solution of the quadratic program. As a result, we achieve a controller suitable for implementation on embedded hardware. We demonstrate its applicability to a domestic heating system. The results indicate a very high quality of the approximation of the primary optimal controller that solves mixed-integer programs online.

关键词： nonlinear predictive control energy control data-based control neural networks classification heat flows control applications

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Stochastic MPC for Energy Management in Smart Grids with Conditional Value at Risk as Penalty Function

Stochastic MPC for Energy Management in Smart Grids with Con...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Janani Venkatasubramanian Vahab Rostampour Tamás Keviczky Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Engineering and Technology Institute University of Groningen Groningen The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781728171005

ISBN: (纸本)9781728171012

This paper considers imbalance problems arising in Energy Management in Smart Grids (SG) as discrete-time stochastic linear systems subject to chance constraints, and proposes a Model Predictive control (MPC) approach to solve them. It is well-known that handling the closed-loop constraint feasibility of such systems is in general difficult due to the presence of a potentially unbounded uncertainty source. To overcome such a difficulty, we propose two new ideas. We first reformulate the chance constraint using the so-called Conditional Value at Risk (CVaR), which is known to be the tightest convex approximation for chance constraints. We then relax the CVaR constraint using a penalty function depending on a coefficient parameter. An optimal solution is therefore obtained by solving a single unconstrained problem which, intuitively, takes into consideration a risk of the system trajectories in an undesirable state. A case study using an academic example is presented to estimate the a-posteriori probability of the coefficient parameter in order to show when such a penalty function is exact by means of probabilistic constraint fulfillment.

关键词： Uncertainty Stochastic processes Probabilistic logic Smart grids Trajectory Energy management Predictive control

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