Residential HVAC load control strategy in real-time electricity pricing environment

2012 ◽  
Author(s):  
Mesut Avci ◽  
Murat Erkoc ◽  
Shihab S. Asfour
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Load Control ◽  
Electricity Pricing

Model predictive HVAC load control in buildings using real-time electricity pricing

2013 ◽  
Vol 60 ◽  
pp. 199-209 ◽  
Author(s):  
Mesut Avci ◽  
Murat Erkoc ◽  
Amir Rahmani ◽  
Shihab Asfour
Keyword(s):  
Real Time ◽  
Load Control ◽  
Electricity Pricing

scholarly journals Adaptive PV Frequency Control Strategy Based on Real-time Inertia Estimation

2020 ◽  
pp. 1-1
Author(s):  
Yu Su ◽  
Hongyu Li ◽  
Yi Cui ◽  
Shutang You ◽  
Yiwei Ma ◽  
...  
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Frequency Control

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

scholarly journals Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Control Strategy ◽  
High Density ◽  
Fine Tuning ◽  
Mode Switching ◽  
Correct Prediction ◽  
Learning Approach ◽  
Predictive Values ◽  
Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

Optimal control of a novel uninterrupted multi-speed transmission for hybrid electric mining trucks

2019 ◽  
Vol 233 (12) ◽  
pp. 3235-3245 ◽  
Author(s):  
Weiwei Yang ◽  
Jiejunyi Liang ◽  
Jue Yang ◽  
Nong Zhang
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Control Strategy ◽  
Dynamic Models ◽  
Fuel Efficiency ◽  
Real Time Control ◽  
Traction Motor ◽  
Time Control ◽  
Power Split ◽  
Shift Strategy

Considering the energy consumption and specific performance requirements of mining trucks, a novel uninterrupted multi-speed transmission is proposed in this paper, which is composed of a power-split device, and a three-speed lay-shaft transmission with a traction motor. The power-split device is adapted to enhance the efficiency of the engine by adjusting the gear ratio continuously. The three-speed lay-shaft transmission is designed based on the efficiency map of traction motor to guarantee the drivability. The combination of the power-split device and three-speed lay-shaft transmission can realize uninterrupted gear shifting with the proposed shift strategy, which benefits from the proposed adjunct function by adequately compensating the torque hole. The detailed dynamic models of the system are built to verify the effectiveness of the proposed shift strategy. To evaluate the maximum fuel efficiency that the proposed uninterrupted multi-speed transmission could achieve, dynamic programming is implemented as the baseline. Due to the “dimension curse” of dynamic programming, a real-time control strategy is designed, which can significantly improve the computing efficiency. The simulation results demonstrate that the proposed uninterrupted multi-speed transmission with dynamic programming and real-time control strategy can improve fuel efficiency by 11.63% and 8.51% compared with conventional automated manual transmission system, respectively.

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