scholarly journals Visual Flight Rules-Based Collision Avoidance Systems for UAV Flying in Civil Aerospace

Robotics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 9 ◽  
Author(s):  
Hamid Alturbeh ◽  
James F. Whidborne
Keyword(s):  
Decision Making ◽  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Collision Avoidance ◽  
Inverse Dynamics ◽  
Guidance System ◽  
Visual Flight Rules ◽  
Decision Making System

The operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation authorities, which require full compliance with regulations that apply for manned aircraft. This paper proposes control algorithms for a collision avoidance system that can be used as an advisory system or a guidance system for UAVs that are flying in civil airspace under visual flight rules. A decision-making system for collision avoidance is developed based on the rules of the air. The proposed architecture of the decision-making system is engineered to be implementable in both manned aircraft and UAVs to perform different tasks ranging from collision detection to a safe avoidance manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are proposed based on pilot suggestions for a subset of possible collision scenarios. The proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal collision avoidance algorithm is developed for real-time local trajectory planning. Essentially, a finite-horizon optimal control problem is periodically solved in real-time hence updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal control problem is formulated in output space, and parameterized by using B-splines. Then the optimal designed outputs are mapped into control inputs of the system by using the inverse dynamics of a fixed wing aircraft.

Fast Online-Implementable Sub-Optimal Energy Management of a Power-Split Hybrid Electric Vehicle

2013 ◽  
Author(s):  
Jian Dong ◽  
Rui Cheng ◽  
Zuomin Dong ◽  
Curran Crawford
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Powertrain ◽  
Power Split

The current focus of HEV controller design is on the development of real-time implementable energy management strategies that can approximate the global optimal solution closely. In this work, the Toyota Prius power-split hybrid powertrain is used as a case study for developing online energy management strategy for hybrid electric vehicle. The power-split hybrid powertrain combines the advantages of both the series and parallel hybrid powertrain and has been appealing to the auto-makers in the past years. The addition of two additional electric machines and a Planetary Gear Sets (PGS) allows more flexibility in terms of control at some cost of complexity. A forward-looking dynamic model of the power-split powertrain system is developed and implemented in Simulink first. An optimal control problem is formulated, which is further reduced to an optimal control problem with a single-variable objective function and a single-state subject to both dynamic constraint and boundary constraint. The reduced optimal control problem is then solved by an on-line (real-time) implementable approach based on Pontryagin’s Minimum Principal (PMP), where the costate p is adapted based on SOC feedback. Simulation results on standard driving cycles are compared using the proposed optimal control strategy and a rule-based control strategy. The results have shown significant improvement in fuel economy comparing to the baseline vehicle model, and the proposed online (real-time) PMP control algorithm with an adaptive costate p is very close to the optimal PMP solution with a constant costate. The proposed optimal control has a fast computation speed and calculates the optimal decision “dynamically” without the necessity of knowing future driving cycle information and can be practically implemented in real-time.

Study on the optimal lane changing steering input under different road adhesion coefficients

2019 ◽  
Vol 234 (3) ◽  
pp. 384-395
Author(s):  
Xinglong Zhang ◽  
Youqun Zhao ◽  
Wenxin Zhang ◽  
Fen Lin ◽  
Liguo Zang
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Inverse Dynamics ◽  
Pseudospectral Method ◽  
Reference Value ◽  
Unmanned Vehicles ◽  
Control Input ◽  
Computational Accuracy ◽  
Lane Changing

To study the influence of road adhesion coefficient on lane changing steering input, vehicle handling inverse dynamics based on hp-adaptive Radau pseudospectral method is proposed in this article. First, a steering inverse dynamics model is established and then the inverse dynamics problem is converted into an optimal control problem. Second, by applying the hp-adaptive Radau pseudospectral method, the optimal control problem is converted into a nonlinear programming problem, which is then solved through sequential quadratic programming. Besides, two contrast verifications are carried out to validate the correctness and advantages of the proposed method. The simulation results show that the obtained control input satisfies the vehicle dynamic constraints. And comparing with Gauss Pseudospectral Method (GPM), hp-adaptive Radau pseudospectral method has higher computational accuracy and computational efficiency when solving non-smooth problem. The study of optimal steering input can provide guidance for driver’s lane changing behavior, so that the accident rate caused by human factors can be decreased. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.

Vehicle Trajectory Specification in Presence of Traffic Lights with Known or Uncertain Switching Times

2020 ◽  
Vol 2674 (8) ◽  
pp. 53-66
Author(s):  
Panagiotis Typaldos ◽  
Ioanna Kalogianni ◽  
Kyriakos Simon Mountakis ◽  
Ioannis Papamichail ◽  
Markos Papageorgiou
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Switching Time ◽  
Minimum Principle ◽  
Stochastic Dynamic ◽  
Traffic Lights ◽  
Switching Times ◽  
Optimal Approach

The main purpose of this work is to generate optimal trajectories for vehicles crossing a signalized junction, with traffic signals operated in either fixed-time or real-time (adaptive) mode. In the latter case, the next switching time is decided in real time based on the prevailing traffic conditions and is therefore uncertain in advance. The GLOSA (Green Light Optimal Speed Advisory) problem is addressed by using traffic lights information and calculating a trajectory and velocity profile for the vehicle based on the vehicle’s initial state (position and speed) and a fixed final destination state. At first, an appropriate optimal control problem is formulated and solved analytically via Pontryagin’s minimum principle (PMP) for the case of known switching times. Subsequently, for the case of real-time signals, availability of a time-window of possible signal switching times, along with the corresponding probability distribution, is assumed, and the problem is cast in the format of a stochastic optimal control problem and is solved numerically using stochastic dynamic programming (SDP) techniques. Application results, for various driving scenarios, of the deterministic approach, which considers the case of known switching times, and a comprehensive comparison of the stochastic GLOSA approach with a sub-optimal approach are presented. In particular, it is demonstrated that the proposed SDP approach achieves better average performance compared with the sub-optimal approach because of the better (probabilistic) information on the traffic light switching time.

scholarly journals Model Order Reduction for Stochastic Optimal Control

2012 ◽  
Author(s):  
Ngoc-Hien Nguyen ◽  
Karen Willcox ◽  
Boo Cheong Khoo
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Stochastic Optimal Control ◽  
Reduced Order Model ◽  
Full Model ◽  
Order Model ◽  
Reduced Order ◽  
Flow Quality

This work presents an approach to solve stochastic optimal control problems in the application of flow quality management in reservoir systems. These applications are challenging because they require real-time decision-making in the presence of uncertainties such as wind velocity. These uncertainties must be accounted for as stochastic variables in the mathematical model. In addition, computational costs and storage requirements increase rapidly due to the stochastic nature of the simulations and optimisation formulations. To overcome these challenges, an approach is developed that uses the combination of a reduced-order model and an adjoint-based method to compute the optimal solution rapidly. The system is modelled by a system of stochastic partial differential equations. The finite element method together with collocation in the stochastic space provide an approximate numerical solution—the “full model”, which cannot be solved in real-time. The proper orthogonal decomposition and Galerkin projection technique are applied to obtain a reduced-order model that approximates the full model. The conjugate-gradient method with Armijo line-search is then employed to find the solution of the optimal control problem under the uncertainty of input parameters. Numerical results show that the stochastic control yields solutions that are above the bound of the set solutions of the deterministic control. Applying the reduced model to the stochastic optimal control problem yields a speed-up in computational time by a factor of about 80 with acceptable accuracy in comparison with the full model. Application of the optimal control strategy shows the potential effectiveness of this computational modeling approach for managing flow quality.

scholarly journals Optimal Transient Real-Time Engine-Generator Control in the Series-Hybrid Vehicle

2019 ◽  
Author(s):  
Jonathan Lock ◽  
Rickard Arvidsson ◽  
Tomas McKelvey
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Fuel Consumption ◽  
Control Period ◽  
Infinite Horizon ◽  
Successive Approximation Method ◽  
Real Time Control ◽  
Time Control

Abstract We study the dynamic engine-generator optimal control problem with a goal of minimizing fuel consumption while delivering a requested average electrical power. By using an infinite-horizon formulation and explicitly minimizing fuel consumption, we avoid issues inherent with penalty-based and finite-horizon problems. The solution to the optimal control problem, found using dynamic programming and the successive approximation method, can be expressed as instantaneous non-linear state-feedback. This allows for trivial real-time control, typically requiring 10–20 CPU instructions per control period, a few bytes of RAM, and 5–20 KiB of nonvolatile memory. Simulation results for a passenger vehicle indicate a fuel consumption improvement in the region of 5–7% during the transient phase when compared with the class of controllers found in the industry. Bench-tests, where the optimal controller is executed in native hardware, show an improvement of 3.7%, primarily limited by unmodeled dynamics. Our specific choice of problem formulation, a guaranteed globally optimal solution, and trivial real-time control resolve many of the limitations with the current state of optimal engine-generator controllers.

Trajectory planning with mid-air collision avoidance for quadrotor unmanned aerial vehicles

2021 ◽  
pp. 095441002110440
Author(s):  
Yuhang Jiang ◽  
Shiqiang Hu ◽  
Christopher J Damaren
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Pseudospectral Method ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Optimal Control Algorithms

Flight collision between unmanned aerial vehicles (UAVs) in mid-air poses a potential risk to flight safety in low-altitude airspace. This article transforms the problem of collision avoidance between quadrotor UAVs into a trajectory-planning problem using optimal control algorithms, therefore achieving both robustness and efficiency. Specifically, the pseudospectral method is introduced to solve the raised optimal control problem, while the generated optimal trajectory is precisely followed by a feedback controller. It is worth noting that the contributions of this article also include the introduction of the normalized relative coordinate, so that UAVs can obtain collision-free trajectories more conveniently in real time. The collision-free trajectories for a classical scenario of collision avoidance between two UAVs are given in the simulation part by both solving the optimal control problem and querying the prior results. The scalability of the proposed method is also verified in the simulation part by solving a collision avoidance problem among multiple UAVs.

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