scholarly journals Optimal dynamic Control Allocation with guaranteed constraints and online Reinforcement Learning

Automatica ◽  
2020 ◽  
Vol 122 ◽  
pp. 109265
Author(s):  
Patrik Kolaric ◽  
Victor G. Lopez ◽  
Frank L. Lewis
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Control ◽  
Control Allocation ◽  
Optimal Dynamic

Proxy-Based Optimal Dynamic Control Allocation for Multi-Input, Multi-Output Over-Actuated Systems

2017 ◽  
Author(s):  
Molong Duan ◽  
Chinedum Okwudire
Keyword(s):  
Dynamic Control ◽  
Control Allocation ◽  
Control Effort ◽  
Controlled System ◽  
Time Optimization ◽  
Optimal Dynamic ◽  
Matrix Fraction ◽  
Optimal Subspace ◽  
Real Time Optimization ◽  
Allocation Approach

In over-actuated systems, an output can be realized through various control effort combinations. It is desirable to allocate the control efforts dynamically (as opposed to statically) in an optimal manner. In this paper, a proxy-based control allocation approach is proposed for multi-input, multi-output over-actuated systems. Instead of using real-time optimization for control allocation, the proposed method establishes an energy optimal subspace; it then defines a causally implementable proxy to accurately measure the deviation of the controlled system from the energy optimal subspace using matrix fraction description and spectral factorization. The control allocation problem is thus converted to a regulation problem, and is solved using a standard H∞ approach. The proposed method is validated through simulation examples, in comparison with an existing dynamic control allocation method. Significant improvements in energy efficiency without affecting the controlled output are demonstrated.

Multi-UAV Cooperative Transportation Using Dynamic Control Allocation and a Reinforcement Learning Compensator

2021 ◽  
Author(s):  
Shuai Li ◽  
Damiano Zanotto
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Control ◽  
Parallel Robots ◽  
Control Allocation ◽  
Control Input ◽  
Tracking Errors ◽  
Tracking Method ◽  
On Line ◽  
Multi Uav ◽  
Allocation Approach

Abstract This paper proposes a new trajectory tracking method for a 6 degree-of-freedom (DOF) cable-suspended payload controlled by a team of quadrotors. Using the modeling convention of reconfigurable cable-driven parallel robots (RCDPRs) the coupled dynamics of the payload and the quadrotors are derived. Based on this dynamic model, a new dynamic control allocation approach is introduced to optimally distribute the virtual control input (i.e., the wrench to be exerted on the payload) among the cables and generate reference positions for the quadrotors on-line, while avoiding collisions between quadrotors and accounting for cable tension constraints. Furthermore, a new reinforcement-learning (RL) compensator is proposed to reduce tracking errors caused by the constraints in the quadrotors’ thrusts. Numerical simulations are conducted to validate the proposed approach.

Model-Predictive Dynamic Control Allocation Scheme for Reentry Vehicles

2007 ◽  
Vol 30 (1) ◽  
pp. 100-113 ◽  
Author(s):  
Yu Luo ◽  
Andrea Serrani ◽  
Stephen Yurkovich ◽  
Michael W. Oppenheimer ◽  
David B. Doman
Keyword(s):  
Dynamic Control ◽  
Control Allocation ◽  
Allocation Scheme ◽  
Reentry Vehicles

scholarly journals Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

2019 ◽  
Vol 35 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Thomas George Thuruthel ◽  
Egidio Falotico ◽  
Federico Renda ◽  
Cecilia Laschi
Keyword(s):  
Reinforcement Learning ◽  
Closed Loop ◽  
Dynamic Control ◽  
Robotic Manipulators ◽  
Model Based

Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles

2018 ◽  
Vol 233 (12) ◽  
pp. 3125-3146 ◽  
Author(s):  
Hui Liu ◽  
Xunming Li ◽  
Weida Wang ◽  
Lijin Han ◽  
Huibin Xin ◽  
...  
Keyword(s):  
Power Management ◽  
Management Strategy ◽  
Dynamic Control ◽  
Control Allocation ◽  
Energy Management Strategy ◽  
Torque Distribution ◽  
Power Management Strategy ◽  
Optimal Power ◽  
Wheel Drive ◽  
Four Wheel Drive

An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.

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