Support vector machine based optimal control for minimizing energy consumption of biped walking motions

Wang, Liyang; Li, Zhi; Chen, C. L. Philip; Zhang, Yun; Lee, Sukhan

doi:10.1007/s12541-012-0260-7

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…Wang et al [90] proposed an optimal control approach based on a support vector algorithm to minimize the power consumption of walking bipedal robots under an unknown system dynamic model conditions and small data sample sizes. The new controller is built into the optimal controller and aims to minimize the energy-related cost function with constraints on robot articulation angles.…”

Section: Optimization Problemsmentioning

confidence: 99%

Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey

Zaitceva

Andrievsky

2022

Electronics

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Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time decision-making, while the other ones may serve as an essential factor in human-robot interaction and human influences on system performance. Naturally, the complex tasks of controlling technical systems require new modern solutions, but there remains an inextricable link between control theory and artificial intelligence. The first part of the present survey is devoted to the main intelligent control methods in technical systems. Among them, modern methods of adaptive and optimal control, fuzzy logic, and machine learning are considered. In its second part, the crucial achievements in intelligent control applications in robotic and mechatronic systems over the past decade are considered. The references are structured according to the type of such common control problems as stabilization, controller tuning, identification, parametric optimization, iterative learning, and prediction. In the conclusion, the main problems and tendencies toward intelligent control methods improvement are outlined.

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Section: Optimization Problemsmentioning

confidence: 99%

Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey

Zaitceva

Andrievsky

2022

Electronics

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“…In this way, the LSSVM achieves simple computation and rapid solving speed. Thus, this algorithm has been widely used in function estimation and approximation [15][16][17].…”

Section: Lssvmmentioning

confidence: 99%

A Navigation Accuracy Evaluation Method for Multi-path Platform Inertial Navigation System

Huang¹,

Yi²,

Zen³

et al. 2019

I2M

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The purpose of this study was to investigate estimation of the navigation accuracy of the highprecision platform inertial navigation system (PINS) rapidly. However, the prediction accuracy often plunges deeply when the model is trained by numerous flight paths. The PP-LSSVM approach was adopted to solve the problem with sparse solutions. The highdimensional input data were dimensionally reduced by the principal component analysis (PCA); The sparsity of the model was improved by the pruning algorithm, aiming to reduce the computing load and prediction time. Thus, the proposed model is denoted as the PP-LSSVM. The results obtained in this study include the PP-LSSVM outperformed the LSSVM in prediction time by an order of magnitude, while satisfying the accuracy requirement. The results indicated that the research provides a suitable evaluation model for navigation accuracy of multi-path PINS.

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“…The sequential methods depend on the historical time series of energy consumption, and they are still prevalent owing to high flexibility and availability. The classical sequential methods include the autoregressive integrated moving average (ARIMA) model [13], the XGBoost model [14], the Kalman filtering model [15], the support vector regression (SVR) model [16][17][18] and the back-propagation (BP) neural network model [19]. However, the above methods cannot fit well with capturing complex features from big data.…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

et al. 2022

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Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.

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Support vector machine based optimal control for minimizing energy consumption of biped walking motions

Cited by 11 publications

References 24 publications

Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey

Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey

A Navigation Accuracy Evaluation Method for Multi-path Platform Inertial Navigation System

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

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