167,273 research outputs found
Trajectory Optimization Using Neural Network Gradients of Learned Dynamics
Trajectory optimization methods have achieved an exceptional level of
performance on real-world robots in recent years. These methods heavily rely on
accurate physics simulators, yet some aspects of the physical world, such as
friction, can only be captured to a limited extent by most simulators. The goal
of this paper is to leverage trajectory optimization for performing highly
dynamic and complex tasks with robotic systems in absence of an accurate
physics simulator. This is achieved by applying machine learning techniques to
learn a differentiable dynamics model of the system from data. On the example
of a RC car, we show that from data collected in only 15 minutes of
human-operated interactions with the car, a neural network is able to model
highly nonlinear behaviors such as loss of traction and drifting. Furthermore,
we use the analytical gradients of the neural network to perform gradient-based
trajectory optimization, both in an offline and online setting. We find that
our learned model is able to represent complex physical behavior, like drifting
and gives unprecedented performance in combination with trajectory optimization
methods
Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Model Predictive Control (MPC) has become a popular framework in embedded control for high-performance autonomous systems. However, to achieve good control performance using MPC, an accurate dynamics model is key. To maintain real-time operation, the dynamics models used on embedded systems have been limited to simple first-principle models, which substantially limits their representative power. In contrast to such simple models, machine learning approaches, specifically neural networks, have been shown to accurately model even complex dynamic effects, but their large computational complexity hindered combination with fast real-time iteration loops. With this work, we present Real-time Neural MPC , a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. Our experiments, performed in simulation and the real world onboard a highly agile quadrotor platform, demonstrate the capabilities of the described system to run learned models with, previously infeasible, large modeling capacity using gradient-based online optimization MPC. Compared to prior implementations of neural networks in online optimization MPC we can leverage models of over 4000 times larger parametric capacity in a 50 Hz real-time window on an embedded platform. Further, we show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics
Modelling of river discharges using neural networks derived from support vector regression
Neural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls.published_or_final_versio
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
Accurate load forecasting plays a vital role in numerous sectors, but
accurately capturing the complex dynamics of dynamic power systems remains a
challenge for traditional statistical models. For these reasons, time-series
models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly
deployed and often experience higher success. In this paper, we analyze the
efficacy of the recently developed Transformer-based Neural Network model in
Load forecasting. Transformer models have the potential to improve Load
forecasting because of their ability to learn long-range dependencies derived
from their Attention Mechanism. We apply several metaheuristics namely
Differential Evolution to find the optimal hyperparameters of the
Transformer-based Neural Network to produce accurate forecasts. Differential
Evolution provides scalable, robust, global solutions to non-differentiable,
multi-objective, or constrained optimization problems. Our work compares the
proposed Transformer based Neural Network model integrated with different
metaheuristic algorithms by their performance in Load forecasting based on
numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage
Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced
Transformer-based Neural Network models in Load forecasting accuracy and
provide optimal hyperparameters for each model.Comment: 6 Pages, 6 Figures, 2 Table
Neural network-based predictive control system for energy optimization in sports facilities: a case study
Given the increased global energy demand and its associated environmental impacts, the
management and optimization of sports facilities is becoming imperative as they are
characterized by high energy demand and occupancy profiles. In this work, the theory of model
predictive control ȋMPCȌ is combined with neural networks for temperature setpoint selection to
achieve energy and performance optimization of sports facilities. It is demonstrated using the
building information model ȋBIMȌ of a sports hall in the sports complex of Qatar University. MPC
systems are powerful as they allow integrated dynamic optimization that accounts for the future
system behavior in the decision-making process, while neural networks are advantageous for
their ability to represent complex interdependencies with high accuracy. The proposed approach
was able to achieve a total energy savings of around ͵͵Ψ. Considerations about the network
performance, MPC settings tuning, and optimization sub-optimality or failure are essential during
the design and implementation phases of the proposed system
Penguasaan kemahiran generik di kalangan graduan hospitaliti di politeknik : satu kajian berkenaan keperluan industri perhotelan, persepsi pensyarah dan pelajar
Kajian yang dijalankan ini bertujuan untuk mengenal pasti kepentingan
kemahiran generik mengikut keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan persepsi pelajar Jabatan Hospitaliti. Oleh kerana matlamat kurikulum pendidikan kini adalah untuk melahirkan graduan yang dapat memenuhi keperluan pihak industri, maka kajian ini dijalankan untuk menilai hubungan di antara keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan pelajar Jabatan Hospitaliti di Politeknik. Terdapat 13 kemahiran generik yang diperolehi daripada Kementerian Pelajaran dan Latihan Ontario (1997) dijadikan
sebagai skop kepada instrumen kajian. Responden kajian terdiri daripada tiga pihak utama iaitu industri perhotelan di Malaysia yang melibatkan 40 buah hotel yang diwakili oleh MAH Chapter dan jawatankuasa dalam Malaysian Associated of Hotel (MAH), pensyarah Unit Hotel dan Katering dan pelajar semester akhir Diploma Hotel dan Katering di Politeknik Johor Bahru, Johor dan Politeknik Merlimau, Melaka. Kajian rintis yang dijalankan menunjukkan nilai Alpha Cronbach pada 0.8781. Data yang diperolehi dianalisis secara deskriptif dan inferensi dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 11.5. Melalui dapatan kajian, satu senarai berkenaan kemahiran generik yang diperlukan
oleh industri perhotelan telah dapat dihasilkan. Selain itu, senarai kemahiran generik menurut persepsi pensyarah dan juga persepsi pelajar turut dihasilkan. Hasil statistik dan graf garis yang diperolehi menunjukkan terdapat perbezaan di antara kemahiran generik yang diperlukan oleh industri perhotelan di Malaysia dengan kemahiran generik menurut persepsi pensyarah dan persepsi pelajar Politeknik. Dapatan analisis menggunakan korelasi Pearson mendapati bahawa tidak terdapat
perhubungan yang signifikan di antara kemahiran generik yang diperlukan oleh industri perhotelan dengan persepsi pensyarah dan persepsi pelajar. Namun begitu, terdapat hubungan yang signifikan di antara persepsi pensyarah dengan persepsi pelajar berkenaan dengan amalan kemahiran generik di Politeknik
- …