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learning-based model predictive control for Legged Robots with Battery-Supercapacitor Hybrid Energy Storage System

APPLIED SCIENCES-BASEL 2025年第1期15卷

作者： Shu, Boyu Huang, Zhiwu Ren, Wanwan Wu, Yue Li, Heng Cent South Univ Sch Automat Changsha 410083 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410075 Peoples R China Cent South Univ Sch Elect Informat Changsha 410075 Peoples R China

Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery-supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. Secondly, a multi-objective optimization objective function is established for the MPC-based energy management strategy. Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. Hardware-in-the-loop experimental implementations demonstrate that the proposed method can improve the kinematic performance of the legged robot by maintaining the supercapacitor state of charge at a relatively high level and reducing the battery capacity loss by 12.7% compared with the conventional MPC method.

关键词： energy management strategy electrically driven legged robot hybrid energy storage system learning-based model predictive control

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learning-based model predictive control for Smart Building Thermal Management 16

Learning-based Model Predictive Control for Smart Building T...

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IEEE 16th International Conference on Smart Cities - Improving Quality of Life Using ICT and IoT and AI (HONET-ICT)

作者： Eini, Roja Abdelwahed, Sherif Virginia Commonwealth Univ Dept Elect Engn Richmond VA 23284 USA

ISBN: (纸本)9781728139715

This paper proposes a learning-based model predictive control (MPC) approach for the thermal control of a four-zone smart building. The objectives are to minimize energy consumption and maintain the residents' comfort. The proposed control scheme incorporates learning with the model-based control. The occupancy profile in the building zones are estimated in a long-term horizon through the artificial neural network (ANN), and this data is fed into the model-based predictor to get the indoor temperature predictions. The Energy Plus software is utilized as the actual dataset provider (weather data, indoor temperature, energy consumption). The optimization problem, including the actual and predicted data, is solved in each step of the simulation and the input setpoint temperature for the heating/cooling system, is generated. Comparing the results of the proposed approach with the conventional MPC results proved the significantly better performance of the proposed method in energy savings (40.56% less cooling power consumption and 16 .73% less heating power consumption), and residents' comfort.

关键词： learning-based model predictive control model-based control Smart building management and control Artificial neural network Occupancy estimation Heating/cooling system

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A learning-based model predictive control scheme for injection speed tracking in injection molding process

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COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 7845-7861页

作者： Ren, Zhigang Cai, Jianpu Zhang, Bo Wu, Zongze Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Technol Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Intelligent Detect & Internet Things Mfg G Guangzhou 510006 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518060 Peoples R China

Injection molding is a pivotal industrial process renowned for its high production speed, efficiency, and automation. controlling the motion speed of injection molding machines is a crucial factor that influences production processes, directly affecting product quality and efficiency. This paper aims to tackle the challenge of achieving optimal tracking control of injection speed in a standard class of injection molding machines (IMMs) characterized by nonlinear dynamics. To achieve this goal, we propose a learning-based model predictive control (LMPC) scheme that incorporates Gaussian process regression (GPR) to predict and model uncertainty in the injection molding process (IMP). Specifically, the scheme formulates a nonlinear tracking control problem for injection speed, utilizing a GPR-based learning residual model to capture uncertainty and provide accurate predictions. It learns the dynamics model and historical data of the IMM, automatically adjusting the injection speed according to target requirements for optimal production control. Additionally, the optimization problem is efficiently solved using a control-constrained differential dynamic programming approach. Finally, we conduct comprehensive numerical experiments to demonstrate the effectiveness and efficiency of the proposed LMPC scheme for controlling injection speed in IMP.

关键词： Injection molding process learning-based model predictive control Optimal control Gaussian process regression Differential dynamic programming

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learning-based model predictive control and User Feedback in Home Automation

Learning-based Model Predictive Control and User Feedback in...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Christopher C. W. Ham Surya P. N. Singh Michael Kearney School of Information Technology and Electrical Engineering and M. Kearney is in the School of Mechanical and Mining Engineering at The University of Queensland Brisbane QLD 4072

ISBN: (纸本)9781467363563

Air conditioning systems are generally the largest systems in a home, both physically and energetically, and thus central to home automation efforts. Finding a balance between user comfort and efficiency is a complex problem given the considerable variation present. This paper focuses on comfort, as opposed to absolute temperature, as gauged using simple, sparse user inputs. A thermal model of the house is learned which accounts for weather data and exogenous factors such as occupancy. By incorporating user feedback, a learning-based model predictive controller (LBMPC) is able adapt to home conditions and more efficiently operate the system. In contrast to previous efforts which operate in office spaces and to a set point, this work is adapted and tested in a typical home environment and closes a control loop on user comfort. The controller considers that the user's comfort levels may change during the day, for example when the user is in bed, or not at home. It shows that complex systems may be automated without extensive tweaking by the user and in a manner that considers user comfort, time of day, and related factors to reduce energy consumption.

关键词： learning-based model predictive control Home Automation conditioning systems

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A learning-based model predictive control scheme and its application in biped locomotion

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期115卷

作者： Li, Jingchao Yuan, Zhaohui Dong, Sheng Sang, Xiaoyue Kang, Jian Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China

This paper proposes a learning-based model predictive control scheme. This scheme divides the predictive model into a known nominal model and an unknown model residual. model residual is learned using Gaussian process regression. The learned stochastic model is solved quickly using differential dynamic programming, taking into account control input constraints. The simulation results show that compared with state of art optimal control methods, this scheme has good robustness to model residual, accelerates the solution of high-dimensional problems, and can strictly constrain the control inputs according to the actual situation. based on this learning-based model predictive control scheme, this paper also proposes an online learning gait generator for the uncertainty problem in the locomotion control of biped robots. The zero moment point is strictly constrained during training to ensure safety. The simulation results show that the gait generator is robust to unknown load and unknown external force.

关键词： Biped locomotion learning-based model predictive control Gaussian process regression Differential dynamic programming

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A learning-based model predictive Contouring control for Vehicle Evasive Manoeuvres 16th

A Learning-Based Model Predictive Contouring Control for Veh...

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16th International Symposium on Advanced Vehicle control

作者： Bertipaglia, Alberto Alirezaei, Mohsen Happee, Riender Shyroka, Barys Delft Univ Technol NL-2628 CD Delft Netherlands Siemens PLM Software NL-5708 JZ Helmond Netherlands

ISBN: (纸本)9783031703942;9783031703928;9783031703911

This paper presents a novel learning-based model predictive Contouring control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC's cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle's manoeuvrability compared to an L-MPCC with a Gaussian Process.

关键词： learning-based model predictive control Student-t process Evasive manoeuvre Obstacle avoidance Limit of handling

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learning-based model predictive secondary frequency control of PV-ESS-EV microgrid

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 159卷

作者： Zhong, Cheng Zhao, Hailong Liu, Yudong Liu, Chuang Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control Jilin 132012 Jilin Peoples R China Northeast Elect Power Univ Renewable Energy Technol Minist Educ Jilin 132012 Jilin Peoples R China

Microgrids comprising photovoltaic (PV), energy -storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning -based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional modelpredictive controllers. The results indicate that the learning -based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method.

关键词： learning-based model predictive control Secondary frequency control Gaussian process

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Dynamic modeling and learning based path tracking control for ROV-based deep-sea mining vehicle

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 262卷

作者： Chen, Yuheng Zhang, Haicheng Zou, Weisheng Zhang, Haihua Zhou, Bin Xu, Daolin Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Hunan Peoples R China Deepsea Technol Sci Lian Yun Gang Ctr Taihu Lab Lianyungang 222005 Peoples R China

Track slippage and body sinking of the tracked mining vehicle in the traditional deep-sea mining system are the critical issues for operating stability. To solve this bottleneck problem, a novel ROV-based deep-sea mining system is proposed in this study, in which a remotely-operated vehicle (ROV) towering a sledge-shaped mining robot (MRT) named ROV-based Deep-sea Mining Vehicle (ROVDMV) is instead of the traditional tracked Deepsea mining vehicle. The design of the ROVDMV can fundamentally overcome the bottleneck problem. However, the complex marine environment and multi-rigid-body design of the ROVDMV pose new challenges for its pathtracking control. Firstly, the dynamic model of the ROVDMV considering the ROV at a fixed depth is established based on the bicycle model, which is mainly used as the control object in the numerical simulation. Secondly, a learning-based path-tracking control strategy is proposed for the path-tracking control of the ROVDMV. In the control strategy, a novel nonparametric learning (NPL) method is introduced to learn the uncertain nonlinear dynamics considering the external disturbances and parametric uncertainty. The NPL method is proven to provide bounded estimated error. Besides, the enhanced NPL method can save approximately 33 % of the computation time, and the average computation time for its optimization control problem is only 12.47 ms. Finally, the numerical results show that the NPL method can learn nonlinear dynamics accurately, and the proposed strategy has proven to be effective.

关键词： Deep-sea mining Dynamic modelling learning-based model predictive control ROV Path tracking

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A Hybrid Neural Network Approach for Adaptive Scenario-based model predictive control in the LPV Framework

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IEEE control SYSTEMS LETTERS 2023年 7卷 1921-1926页

作者： Bao, Yajie Velni, Javad Mohammadpour Clemson Univ Dept Mech Engn Clemson SC 29634 USA

This letter presents a hybrid neural network (NN) approach for adaptive scenario-based model predictive control (SMPC) design of nonlinear systems in the linear parameter-varying (LPV) framework. In particular, a deterministic artificial neural network (ANN)-based LPV model is learned from data as the nominal model. Then, a Bayesian NN (BNN) is used to describe the mismatch between the plant and the LPV-ANN model. Adaptive scenarios are generated online based on the BNN model to reduce the conservativeness of scenario generation. Moreover, a probabilistic safety certificate is incorporated into the scenario generation by ensuring that the trajectories of scenarios contain the trajectory of the system and that all the scenarios satisfy the constraints with a high probability. Furthermore, conditions for the recursive feasibility of the SMPC are given. Experiments on the closed-loop simulations of a two-tank system demonstrate that the proposed approach can better model the behaviors of nonlinear systems than sole ANN/BNN models can, and the SMPC based on the hybrid NN (HyNN) model can improve the control performance compared to the SMPC with a fixed scenario tree.

关键词： Hybrid neural network adaptive scenario-based model predictive control learning-based model predictive control linear parameter-varying framework

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Event-triggered model predictive control with Reinforcement learning for Highway Local Ramp Metering

Event-triggered Model Predictive Control with Reinforcement ...

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第43届中国控制会议

作者： Bo Jin Jiayang Yu Yaqiang He Zuchen Que Song Yang Zhejiang Scientific Research Institute of Transport Institute of Intelligent Transportation Systems College of Civil Engineering and ArchitectureZhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the backdrop of increasing pressure on highway traffic flow control,this paper proposed a reinforcement learning(RL) based event-triggered model predictive control(e MPC) method for the local ramp metering *** into the eMPC controller structure,the RL agent is responsible for providing triggering *** RL agent learns the eventtriggered policy by continuously interacting with the ***,an effective MPC controller is designed to handle the constraints and objectives in the ramp metering problem,and provide optimal control *** applying the novel RLeMPC method,the traffic efficiency can be significantly improved with less computational *** is due to the combination of the optimal control sequences of MPC controller and the intelligent triggering rule of RL agent.

关键词： active traffic control Q-learning learning-based model predictive control data-driven control

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