A subdivision strategy for adjoining cell mapping on the global optimal control in multi‐input‐multi‐output systems

2021 ◽  
Author(s):  
Yongdong Cheng ◽  
Jun Jiang
Keyword(s):  
Optimal Control ◽  
Cell Mapping ◽  
Global Optimal ◽  
Subdivision Strategy ◽  
Adjoining Cell Mapping ◽  
Adjoining Cell

Global optimal control of redundant robot

Robotica ◽  
1996 ◽  
Vol 14 (6) ◽  
pp. 595-601
Author(s):  
Brian H. Rudall
Keyword(s):  
Optimal Control ◽  
Redundant Robot ◽  
Global Optimal

scholarly journals Research on Optimized Control Model of Freeway Based on Dynamic Traffic Demand Estimation

2014 ◽  
Vol 6 ◽  
pp. 797293 ◽  
Author(s):  
Zhu Jiang ◽  
Shubin Li
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Demand Estimation ◽  
Control Model ◽  
Control Module ◽  
Traffic Demand ◽  
The Real ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Global Optimal

According to the estimation information of dynamic traffic demands, a novel optimal control model of freeway was established on the basis of the hierarchical concept. There are four control modules in this model. The OD prediction module predicts the total traffic demands in a long time and determines the upper bound of the future queuing length in advance; the global optimal control module predicts the future traffic state and establishes the coordination constraints for each ramp in the network; the traffic demand estimation module estimates the real-time traffic conditions for each ramp; the local adaptive control module regulates ramp metering rate according to the estimated information of the real-time traffic conditions and the results optimized by the global optimal control module. The simulation results show that this control system is of a good dynamic performance. It coordinates the benefits of various ramps and optimizes the overall performance of the freeway network.

Variation-free iterative method for global optimal control

1989 ◽  
Vol 50 (5) ◽  
pp. 1731-1743 ◽  
Author(s):  
EFIM A. GALPERIN ◽  
QUAN ZHENG
Keyword(s):  
Optimal Control ◽  
Iterative Method ◽  
Global Optimal

Optimal motion planning by reinforcement learning in autonomous mobile vehicles

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 159-170 ◽  
Author(s):  
M. Gómez ◽  
R. V. González ◽  
T. Martínez-Marín ◽  
D. Meziat ◽  
S. Sánchez
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Mapping Technique ◽  
Cell Mapping ◽  
Optimal Motion ◽  
State Equations ◽  
Learning Stage ◽  
Optimal Motion Planning ◽  
Mapping Techniques ◽  
Adjoining Cell Mapping

SUMMARYThe aim of this work has been the implementation and testing in real conditions of a new algorithm based on the cell-mapping techniques and reinforcement learning methods to obtain the optimal motion planning of a vehicle considering kinematics, dynamics and obstacle constraints. The algorithm is an extension of the control adjoining cell mapping technique for learning the dynamics of the vehicle instead of using its analytical state equations. It uses a transformation of cell-to-cell mapping in order to reduce the time spent during the learning stage. Real experimental results are reported to show the satisfactory performance of the algorithm.

scholarly journals Towards global optimal control via Koopman lifts

Automatica ◽  
2021 ◽  
Vol 132 ◽  
pp. 109610
Author(s):  
Mario Eduardo Villanueva ◽  
Colin N. Jones ◽  
Boris Houska
Keyword(s):  
Optimal Control ◽  
Global Optimal
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