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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 379-389页

作者： Tian, Fangyin Li, Zhiheng Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Controlfor Complex Syst Beijing 100080 Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China

Steering control for autonomous vehicles at high speeds is challenging due to the highly nonlinear vehicle dynamics. The traditional model-based controllers usually degrade significantly in this case. With the development of artificial intelligence, learning-based control methods are emerging as promising alternatives. These methods require a tremendous amount of training data to achieve acceptable performances. However, the data collection process is costly or inefficient. To solve this problem, we propose a parallel learning-based steering control method. Specifically, we first build a neural network (NN) based trajectory generative model (GeneratingNN) based on limited steering-trajectory raw data. The GeneratingNN can efficiently generate sufficient steering-trajectory data by enumerating the allowable steering actions sequences. Then, based on the raw data and generated data, we train another NN (RecallingNN) to learn the inverse mapping relationship between steering actions and trajectories. Hence, the RecallingNN can efficiently recall appropriate steering actions once given the previewed trajectory points. In addition, to further enhance the control accuracy and robustness, we use a simple feedback controller to handle the unmodeled dynamics and external disturbance. Testing results validate that the proposed method can achieve better tracking accuracy, stability and computational efficiency.

关键词： Vehicle dynamics Trajectory Data models Training data Autonomous vehicles Training Predictive models Autonomous driving learning based control parallel learning steering control

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Adaptive neural network based control approach for building energy control under changing environmental conditions 6

Adaptive neural network based control approach for building ...

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6th Annual learning for Dynamics and control Conference

作者： Frison, Lilli Goelzhaeuser, Simon Fraunhofer Inst Solar Energy Syst ISE Heidenhofstr 2 D-79110 Freiburg Germany IMTEK Dept Microsyst Engn Syst Control & Optimizat Lab Georges Koehler Allee 102 D-79110 Freiburg Germany

Deep neural networks are adept at modeling complex relationships between input and output variables. When trained on diverse datasets, they can understand not just the specifics of individual objects but also the broader principles governing an entire object class. This research applies this principle to building heating control, a domain marked by significant heterogeneity and constant environmental changes, including renovations and changes in user behavior. Our approach involves training the network on a wide range of data instances, enhancing its adaptability to newly distributed data representing unseen scenarios. We find that Transformer-based LSTM architectures are particularly adept for this task as they are able to remember previous tasks' learning. We propose a simple yet effective control algorithm that separates system identification and forecasting from the optimization-based control step. This separation simplifies the control process while ensuring robust performance. In a wide range of simulation experiments, we demonstrate that our "universally trained" neural network control can adjust to changing conditions, thus reducing the need for more complex continual learning techniques. Our results suggest that training neural networks on varied datasets empowers the network with the ability to generalize and adapt beyond specific training instances, which demonstrates their effectiveness in dynamic and heterogeneous environments.

关键词： learning based control adaptive control building energy control neural network based control

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Applications of Linear Adaptive Dynamic Programming (ADP) to a Non-linear Four-bar Mechanism 11

Applications of Linear Adaptive Dynamic Programming (ADP) to...

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11th International Conference on control, Mechatronics and Automation (ICCMA)

作者： Sveen, Emil Muhlbradt Zhou, Jing Univ Agder Fac Sci & Engn Kristiansand Norway

ISBN: (纸本)9798350315684

This paper investigates the feasibility of using learning based Adaptive Dynamic Programming (ADP) for linear time-invariant systems to control the position of a highly non-linear four-bar system. An off-line off-policy controller iteration approach, inspired by Reinforcement learning, is used to find a near-optimal controller for the four-bar crank motor. The results show that convergence of a near-optimal linear control policy is possible under the chosen exploration strategy, and the paper further exploit the non-linearity of the four-bar to investigate the limits of the exploration strategy parameters to shed light on the non-triviality of selecting a suitable exploration strategy in ADP. The efficiency of the ADP approach is demonstrated, as just 1.4 seconds of simulation time is required to convergence to a near-optimal ADP controller.

关键词： learning based control non-linear control adaptive dynamic programming

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learning Over All Stabilizing Nonlinear controllers for a Partially-Observed Linear System

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IEEE control SYSTEMS LETTERS 2022年 7卷 91-96页

作者： Wang, Ruigang Barbara, Nicholas H. Revay, Max Manchester, Ian R. Univ Sydney Sydney Inst Robot & Intelligent Syst Australian Ctr Field Robot Sydney NSW 2006 Australia Univ Sydney Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia

This letter proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees. The policy is based on a nonlinear version of the Youla parameterization, and augments a known stabilizing linear controller with a nonlinear operator from a recently developed class of dynamic neural network models called the recurrent equilibrium network (REN). We prove that RENs are universal approximators of contracting and Lipschitz nonlinear systems, and subsequently show that the proposed Youla-REN architecture is a universal approximator of stabilizing nonlinear controllers. The REN architecture simplifies learning since unconstrained optimization can be applied, and we consider both a model-based case where exact gradients are available and reinforcement learning using random search with zeroth-order oracles. In simulation examples our method converges faster to better controllers and is more scalable than existing methods, while guaranteeing stability during learning transients.

关键词： Stability criteria Reinforcement learning Linear systems Computer architecture Training Robots Recurrent neural networks Contraction learning based control Youla parameterization nonlinear output feedback

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Data-Driven Aggregation control for Thermoelectric Loads in Demand Response 1

Data-Driven Aggregation Control for Thermoelectric Loads in ...

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1st IFAC Workshop on control of Complex Systems (COSY)

作者： Cordoba-Pacheco, Andres Diaz-Londono, Cesar Ruiz, Fredy Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

Within the concept of a smart grid, aggregators have the task of coordinating the behavior of large sets of Distributed Energy Resources, each of them offering small power/energy capacities, which help to balance the power grid and can serve as providers of services. Adequate coordination strategies are required to optimally exploit these resources in the ancillary services market. However, deriving model-based control policies for them is complex due to the heterogeneity and uncertainty related to the large set of associated agents. Then, a data-driven model is an adequate solution for this sort of situation. This paper presents the application of the Youla-Kucera Data-Driven control strategy for the development of an aggregator to regulate the power consumption of a set of thermoelectric refrigerators, avoiding the modeling process and directly designing a controller from data. A detailed simulation framework was executed to verify the validity of the proposed methodology. It is shown that the derived aggregator is able to offer frequency containment reserves service, achieving the required settling time of 30 seconds and with a tracking error below 4.7%. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Data-driven control learning based control Youla Kucera parametrization Aggregator Thermometric refrigeration Flexible loads

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Neural Predictor for Flight control With Payload

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第7期10卷 7055-7062页

作者： Jin, Ao Li, Chenhao Wang, Qinyi Liu, Ya Huang, Panfeng Zhang, Fan Northwestern Polytech Univ Res Ctr Intelligent Robot Sch Astronaut Shaanxi Prov Innovat Team Intelligent Robot Techno Xian 710072 Peoples R China

Aerial robotics for transporting suspended payloads as the form of freely-floating manipulator are growinggreat interest in recent years. However, the force/torque caused by payload and residual dynamics will introduce unmodeled perturbations to the aerial robotics, which negatively affects the closed-loop performance. Different from estimation-like methods, this letter proposes Neural Predictor, a learning-based approach to model force/torque induced by payload and residual dynamics as a dynamical system. It yields a hybrid model that combines the first-principles dynamics with the learned dynamics. The hybrid model is then integrated into a MPC framework to improve closed-loop performance. Effectiveness of proposed framework is verified extensively in both numerical simulations and real-world flight experiments. The results indicate that our approach can capture force/torque caused by suspended payload and residual dynamics accurately, respond quickly to the changes of them and improve the closed-loop performance significantly. In particular, Neural Predictor outperforms a state-of-the-art learning-based estimator and has reduced the force and torque estimation errors by up to 66.15% and 33.33% while requiring less samples.

关键词： Payloads Aerodynamics Torque Quadrotors Force Linear systems Predictive models Nonlinear dynamical systems Autonomous aerial vehicles Training learning based control Model learning for control Model Predictive control

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Data-Driven Aggregation control for Thermoelectric Loads in Demand Response

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IFAC-PapersOnLine 2022年第40期55卷 205-210页

作者： Andres Cordoba-Pacheco Cesar Diaz-Londono Fredy Ruiz Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Piazza Leonardo da Vinci 32 20133 Milano Italy

Within the concept of a smart grid, aggregators have the task of coordinating the behavior of large sets of Distributed Energy Resources, each of them offering small power/energy capacities, which help to balance the power grid and can serve as providers of services. Adequate coordination strategies are required to optimally exploit these resources in the ancillary services market. However, deriving model-based control policies for them is complex due to the heterogeneity and uncertainty related to the large set of associated agents. Then, a data-driven model is an adequate solution for this sort of situation. This paper presents the application of the Youla–Kucera Data-Driven control strategy for the development of an aggregator to regulate the power consumption of a set of thermoelectric refrigerators, avoiding the modeling process and directly designing a controller from data. A detailed simulation framework was executed to verify the validity of the proposed methodology. It is shown that the derived aggregator is able to offer frequency containment reserves service, achieving the required settling time of 30 seconds and with a tracking error below 4.7%.

关键词： Data-driven control learning based control Youla Kucera parametrization Aggregator Thermometric refrigeration Flexible loads

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Design of a Wheeled Inverted Pendulum as a Platform for learning based control

Design of a Wheeled Inverted Pendulum as a Platform for Lear...

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IEEE International Conference on Information and Automation (ICIA)

作者： Ouyang, Liping Du, Bianjing Peng, Ansi Ou, Yongsheng Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100864 Peoples R China

ISBN: (纸本)9781467322379

Recently, a robust system as a platform to study multi learning based control methods was developed in our research. learning based intelligent control is a challenging research topic. It is a kind of intelligent control approaches by learning a mapping between world states and actions from demonstrations. To carry out experiments, many platforms had been presented in past research. But most of them are either too complex or too expensive that researchers have to pay more attention on platforms rather than research. Thus, a low cost but robust and multi-purpose platform is quite helpful and valuable for such a research topic. Wheeled Inverted Pendulum(WIP), with clear model and dynamically stable static unstable characteristic, may be an ideal choice. This system uses two paralleled wheels to bear weight, keep balance and make movements at the same time. However, In this paper, we present an implementation of the design of a low cost, high quality WIP with special consideration for learning based controlling research with experiments illustrated.

关键词： WIP learning based control state description kalman filter

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A basic Growing Functional Modules "artificial brain"

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NEUROCOMPUTING 2015年第Part1期151卷 55-61页

作者： Leboeuf-Pasquier, Jerome Univ Guadalajara Guadalajara Jalisco Mexico

The purpose of this paper is to exhibit the internal process of the Growing Functional Modules (GFM) controllers. These learning based controllers are graphically designed by the user, interconnecting four kinds of components: Global Goals, Acting Modules, Sensing Modules and Sensations. Global Goals specify the intrinsic motivations, Acting and Sensing Modules develop their respective functionalities while Sensations provide the controlled system's feedback. GFM controllers engage in a learning process to satisfy the specified motivations while interacting with the environment and thus can be considered as artificial brains. The traditional systems programming task is replaced by a design phase that consists in graphically configuring and interconnecting the GFM components. This paper gives a description of the aforementioned components and of their specific roles in the learning based control process. The behavior of the controller and its components is exemplified with the characterization of a simple control application. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Developmental robotics learning based control Machine learning Neural networks

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control of a Two-Wheeled Self-Balancing Robot with Support Vector Regression Method 4

Control of a Two-Wheeled Self-Balancing Robot with Support V...

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4th IEEE International Conference on Information Science and Technology (ICIST)

作者： Cui, Liangliang Ou, Yongsheng Xin, Junbo Dai, Dawei Gao, Xiang Chinese Acad Sci Guangdong Prov Key Labortory Robot & Intelligent Shenzhen Inst Adv Technol Beijing 100864 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China SSouth China Univ Technol Sch Mech & Automot Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479948086

Recently, learning based control is a popular topic on robotic applications. This paper presents a novel learning based intelligent control method which realizes the balance control of a statically unstable and dynamically stable robot-a two-wheeled self-balancing robot. The control strategy could be segmented into two levels: a learning based controller using Support Vector Regression approach as a high level and a traditional PD controller as a low level. Support Vector Regression is utilized to learn the mapping between robot's state data and corresponding actions from experiments by using the inclined angle and its angular speed as inputs and the wheels velocity of the robot needed to keep balance as outputs. And the low level PD controller makes sure the motors achieve the velocity value gained before. Experiments are taken to show that the control method is useful and efficient. Additionally, this paper presents a practice of learning based control.

关键词： learning based control two-wheeled self-balancing robot Kalman filter support vector regression

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