Hierarchical control strategy towards safe driving of autonomous vehicles

Abstract This work presents a distributed hierarchical control strategy for fleets of autonomous vehicles cruising on a highway with diverse desired speeds. The goal is to design a control scheme that can be employed in scenarios where only vehicle-to-vehicle communication is available and where vehicles need to negotiate and agree on their positions on the road. To this end, after reaching an agreement on the lane speed with other traffic participants, each vehicle decides whether to keep cruising along the current lane or to move into another one. In the latter case, it negotiates the entry point with others by taking part in a distributed auction. An onboard controller computes an optimal trajectory transferring the vehicle with agreed velocity to the desired lane while avoiding collisions.

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Hierarchical Control Strategy based on Robust MPC and Integral Sliding mode - Application to a Continuous Photobioreactor

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.11.285 ◽

2015 ◽

Vol 48 (23) ◽

pp. 212-217 ◽

Cited By ~ 5

Author(s):

S.E. Benattia ◽

S. Tebbani ◽

D. Dumur

Keyword(s):

Control Strategy ◽

Sliding Mode ◽

Hierarchical Control ◽

Integral Sliding Mode

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An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽

10.3390/en14061788 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1788

Author(s):

Gomatheeshwari Balasekaran ◽

Selvakumar Jayakumar ◽

Rocío Pérez de Prado

Keyword(s):

Execution Time ◽

Autonomous Vehicles ◽

Rapid Development ◽

Single Layer ◽

Battery Life ◽

Learning Approaches ◽

Task Management ◽

Inference System ◽

Safe Driving ◽

Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

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Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211029791 ◽

2021 ◽

pp. 095440702110297

Author(s):

Hui Liu ◽

Rui Liu ◽

Riming Xu ◽

Lijin Han ◽

Shumin Ruan

Keyword(s):

Fuel Consumption ◽

Energy Management ◽

Control Strategy ◽

Power Distribution ◽

Hybrid Electric Vehicle ◽

Hierarchical Control ◽

Management Problem ◽

Energy Management Strategy ◽

Dual Mode ◽

Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

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A Novel Hierarchical Control Strategy for Low-Voltage Islanded Microgrids Based on the Concept of Cyber Physical System

Energies ◽

10.3390/en11071835 ◽

2018 ◽

Vol 11 (7) ◽

pp. 1835 ◽

Cited By ~ 1

Author(s):

Qiuxia Yang ◽

Dongmei Yuan ◽

Xiaoqiang Guo ◽

Bo Zhang ◽

Cheng Zhi

Keyword(s):

Control Strategy ◽

Physical System ◽

Reactive Power ◽

Low Voltage ◽

Hierarchical Control ◽

Physical Layer ◽

Cyber Physical System ◽

Droop Control ◽

Control Effect ◽

Consensus Control

Based on the concept of cyber physical system (CPS), a novel hierarchical control strategy for islanded microgrids is proposed in this paper. The control structure consists of physical and cyber layers. It’s used to improve the control effect on the output voltages and frequency by droop control of distributed energy resources (DERs), share the reactive power among DERs more reasonably and solve the problem of circumfluence in microgrids. The specific designs are as follows: to improve the control effect on voltages and frequency of DERs, an event-trigger mechanism is designed in the physical layer. When the trigger conditions in the mechanism aren’t met, only the droop control (i.e., primary control) is used in the controlled system. Otherwise, a virtual leader-following consensus control method is used in the cyber layer to accomplish the secondary control on DERs; to share the reactive power reasonably, a method of double virtual impedance is designed in the physical layer to adjust the output reactive power of DERs; to suppress circumfluence, a method combined with consensus control without leader and sliding mode control (SMC) is used in the cyber layer. Finally, the effectiveness of the proposed hierarchical control strategy is confirmed by simulation results.

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An Adaptive Model Predictive Control Strategy for Path Following of Autonomous Vehicles Based on Tire Cornering Stiffness Estimation

10.1109/ccdc52312.2021.9601550 ◽

2021 ◽

Author(s):

Yuhang Zhang ◽

Weida Wang ◽

Chao Yang ◽

Mingyue Ma

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Autonomous Vehicles ◽

Path Following ◽

Adaptive Model ◽

Adaptive Model Predictive Control

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Hierarchical Control Strategy for Microgrid

2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) ◽

10.1109/imcec.2018.8469405 ◽

2018 ◽

Author(s):

Zhang Mingsheng ◽

Feng Peilei ◽

Wu Hesong ◽

Wan Wenkui ◽

Cai Pengcheng

Keyword(s):

Control Strategy ◽

Hierarchical Control

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Coordinated Frequency Control Strategy with the Virtual Battery Model of Inverter Air Conditionings

Applied Sciences ◽

10.3390/app9153052 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3052

Author(s):

Jiafu Yin ◽

Dongmei Zhao

Keyword(s):

Control Strategy ◽

Frequency Control ◽

Hierarchical Control ◽

Frequency Regulation ◽

Consensus Protocol ◽

Consensus Control ◽

Response Characteristics ◽

Battery Model ◽

Control Framework ◽

Control Scheme

Due to the potential of thermal storage being similar to that of the conventional battery, air conditioning (AC) has gained great popularity for its potential to provide ancillary services and emergency reserves. In order to integrate numerous inverter ACs into secondary frequency control, a hierarchical distributed control framework which incorporates a virtual battery model of inverter AC is developed. A comprehensive derivation of a second-order virtual battery model has been strictly posed to formulate the frequency response characteristics of inverter AC. In the hierarchical control scheme, a modified control performance index is utilized to evaluate the available capacity of traditional regulation generators. A coordinated frequency control strategy is derived to exploit the complementary and advantageous characteristics of regulation generators and aggregated AC. A distributed consensus control strategy is developed to guarantee the fair participation of heterogeneous AC in frequency regulation. The finite-time consensus protocol is introduced to ensure the fast convergence of power tracking and the state-of-charge (SOC) consistency of numerous ACs. The effectiveness of the proposed control strategy is validated by a variety of illustrative examples.

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