Real-time trajectory optimization under input constraints for a flatness-controlled laboratory helicopter

2009 ◽  
Author(s):  
Knut Graichen ◽  
Thomas Kiefer ◽  
Andreas Kugi
Keyword(s):  
Real Time ◽  
Trajectory Optimization ◽  
Input Constraints

Real-Time Onboard Trajectory Optimization Using Indirect Methods

2021 ◽  
Author(s):  
Sean M. Nolan ◽  
Clayton A. Smith ◽  
Jacob D. Wood
Keyword(s):  
Real Time ◽  
Trajectory Optimization ◽  
Indirect Methods

scholarly journals Numerical Simulation of Real-Time Trajectory Optimization for Helicopter Noise Abatement

2010 ◽  
Vol 3 (2) ◽  
pp. 415-430 ◽  
Author(s):  
Hiroshi IKAIDA ◽  
Takeshi TSUCHIYA ◽  
Hirokazu ISHII ◽  
Hiromi GOMI ◽  
Yoshinori OKUNO
Keyword(s):  
Numerical Simulation ◽  
Real Time ◽  
Trajectory Optimization ◽  
Noise Abatement ◽  
Helicopter Noise

Real-time trajectory optimization of an underactuated rigid spacecraft using differential flatness

2012 ◽  
Vol 23 (1) ◽  
pp. 132-139 ◽  
Author(s):  
Yufei Zhuang ◽  
Guangfu Ma ◽  
Haibin Huang ◽  
Chuanjiang Li
Keyword(s):  
Real Time ◽  
Trajectory Optimization ◽  
Differential Flatness ◽  
Rigid Spacecraft

scholarly journals A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6008
Author(s):  
Margherita Montani ◽  
Leandro Ronchi ◽  
Renzo Capitani ◽  
Claudio Annicchiarico
Keyword(s):  
Real Time ◽  
Trajectory Planning ◽  
Vehicle Dynamics ◽  
Trajectory Optimization ◽  
Computational Cost ◽  
Path Tracking ◽  
Driver Model ◽  
Current Scenario ◽  
Optimum Model ◽  
Time Planning

The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.

scholarly journals An Empirical Study on Low Emission Taxiing Path Optimization of Aircrafts on Airport Surfaces from the Perspective of Reducing Carbon Emissions

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1649 ◽  
Author(s):  
Nan Li ◽  
Yu Sun ◽  
Jian Yu ◽  
Jian-Cheng Li ◽  
Hong-fei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Emission Reduction ◽  
Trajectory Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Path Optimization ◽  
Pollutant Emissions ◽  
Future Research ◽  
Aircraft Emissions ◽  
Fuel Consumption And Emissions

Aircraft emissions are the main cause of airport air pollution. One of the keys to achieving airport energy conservation and emission reduction is to optimize aircraft taxiing paths. The traditional optimization method based on the shortest taxi time is to model the aircraft under the assumption of uniform speed taxiing. Although it is easy to solve, it does not take into account the change of the velocity profile when the aircraft turns. In view of this, this paper comprehensively considered the aircraft’s taxiing distance, the number of large steering times and collision avoidance in the taxi, and established a path optimization model for aircraft taxiing at airport surface with the shortest total taxi time as the target. The genetic algorithm was used to solve the model. The experimental results show that the total fuel consumption and emissions of the aircraft are reduced by 35% and 46%, respectively, before optimization, and the taxi time is greatly reduced, which effectively avoids the taxiing conflict and reduces the pollutant emissions during the taxiing phase. Compared with traditional optimization methods that do not consider turning factors, energy saving and emission reduction effects are more significant. The proposed method is faster than other complex algorithms considering multiple factors, and has higher practical application value. It is expected to be applied in the more accurate airport surface real-time running trajectory optimization in the future. Future research will increase the actual interference factors of the airport, comprehensively analyze the actual situation of the airport’s inbound and outbound flights, dynamically adjust the taxiing path of the aircraft and maintain the real-time performance of the system, and further optimize the algorithm to improve the performance of the algorithm.

Real-Time Velocity Optimization for Energy-Efficient Control of Connected and Automated Vehicles

2021 ◽  
Author(s):  
Shiying Dong ◽  
Bing Zhao Gao ◽  
Hong Chen ◽  
Yanjun Huang ◽  
Qifang Liu
Keyword(s):  
Real Time ◽  
Numerical Algorithm ◽  
Energy Efficient ◽  
Trajectory Optimization ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Automated Vehicles ◽  
Pontryagin Minimum Principle ◽  
Optimal Inputs ◽  
Efficient Control

Abstract This paper presents a fast numerical algorithm for velocity optimization based on the Pontryagin' minimum principle (PMP). Considering the difficulties in the application of the PMP when state constraints exist, the penalty function approach is proposed to convert the state-constrained problem into an unconstrained one. Then this paper proposes an iterative numerical algorithm by using the explicit solution to find the optimal solution. The proposed numerical algorithm is applied to the velocity trajectory optimization for energy-efficient control of connected and automated vehicles (CAVs). Simulation results indicate that the algorithm can generate the optimal inputs in milliseconds, and a significant improvement in computational efficiency compared with traditional methods (a few seconds). Hardware in the Loop test for experimental validation is given to further verify the real-time performance of the proposed algorithm.

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