Dynamic Simulation of Blanket Module Maintenance inside Fusion Vacuum Vessel

2014 ◽  
Vol 556-562 ◽  
pp. 1220-1225
Author(s):  
Yan He Zhu ◽  
Lan Ming Guo ◽  
Jie Zhao
Keyword(s):  
Control Strategy ◽  
Hierarchical Control ◽  
Critical Issue ◽  
Vacuum Vessel ◽  
Positioning Accuracy ◽  
Experiment Platform ◽  
Fusion Device ◽  
Remote Maintenance ◽  
Control Scheme ◽  
Essential Problem

The most critical issue of the blanket module remote maintenance operation is to remove or replace the heavy module with high positioning accuracy of 1mm. Located in vacuum vessel (VV) of the nuclear fusion device, the blanket module is weight up to 500kg, thus the grasp and installation of blanket module come to be the essential problem during the maintenance operation. To meet the requirement, we propose a new hierarchical control strategy of rough and fine positioning technology based on combined sensors. The detail procedures and implementation of the control scheme has been carried out successfully on Virtual Robot Experiment Platform to demonstrate the feasibility of the control strategy.

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

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.

Automating lane changes and collision avoidance on highways via distributed agreement

2019 ◽  
Vol 67 (12) ◽  
pp. 1047-1057
Author(s):  
Fabio Molinari ◽  
Aaron Grapentin ◽  
Alexandros Charalampidis ◽  
Jörg Raisch
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Hierarchical Control ◽  
Lane Changes ◽  
The Road ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Control Scheme ◽  
On The Road

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.

scholarly journals Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation

2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Strategy ◽  
External Disturbance ◽  
Control Agent ◽  
Network Control ◽  
Outdoor Environment ◽  
Disturbance Compensation ◽  
Tracking Accuracy ◽  
Control Scheme

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

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

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%.

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

2021 ◽  
pp. 095965182098818
Author(s):  
Fatma Ezzahra Rhili ◽  
Asma Atig ◽  
Ridha Ben Abdennour ◽  
Fabrice Druaux ◽  
Dimitri Lefebvre
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Thermal Process ◽  
Experimental Validation ◽  
Experimental Simulation ◽  
Neural Controller ◽  
Control Scheme ◽  
Stability Method ◽  
Indirect Adaptive Control ◽  
Adaptive Control Scheme

In this study, an adaptive control based on fuzzy adapting rate for neural emulator of nonlinear systems having unknown dynamics is proposed. The indirect adaptive control scheme is composed by the neural emulator and the neural controller which are connected by an autonomous algorithm inspired from the real-time recurrent learning. In order to ensure stability and faster convergence, a neural controller adapting rate is established in the sense of the continuous Lyapunov stability method. Numerical simulations are included to illustrate the effectiveness of the proposed method. The performance of the proposed control strategy is also demonstrated through an experimental simulation.

Continuous Finite-Time Torque Control for Flexible Assistance Exoskeleton with Delay Variation Input

Robotica ◽  
2020 ◽  
pp. 1-26
Author(s):  
Tao Xue ◽  
ZiWei Wang ◽  
Tao Zhang ◽  
Ou Bai ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Finite Time ◽  
Disturbance Rejection ◽  
High Performance ◽  
Control Strategies ◽  
Fractional Power ◽  
Critical Issue ◽  
Human Robot Interaction ◽  
Torque Control ◽  
Finite Time Stability ◽  
Control Scheme

SUMMARY Accurate torque control is a critical issue in the compliant human–robot interaction scenario, which is, however, challenging due to the ever-changing human intentions, input delay, and various disturbances. Even worse, the performances of existing control strategies are limited on account of the compromise between precision and stability. To this end, this paper presents a novel high-performance torque control scheme without compromise. In this scheme, a new nonlinear disturbance observer incorporated with equivalent control concept is proposed, where the faster convergence and stronger anti-noise capability can be obtained simultaneously. Meanwhile, a continuous fractional power control law is designed with an iteration method to address the matched/unmatched disturbance rejection and global finite-time convergence. Moreover, the finite-time stability proof and prescribed control performance are guaranteed using constructed Lyapunov function with adding power integrator technique. Both the simulation and experiments demonstrate enhanced control accuracy, faster convergence rate, perfect disturbance rejection capability, and stronger robustness of the proposed control scheme. Furthermore, the evaluated assistance effects present improved gait patterns and reduced muscle efforts during walking and upstair activity.

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