How Should Rate Constraints be Implemented in Nonlinear Optimal Control Solvers?

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

Gündüz

2017

Self Cite

Abstract-Communication energy in a wireless network of mobile autonomous agents should be considered as the sum of transmission energy and propulsion energy used to facilitate the transfer of information. Accordingly, communication-theoretic and Newtonian dynamic models are developed to model the communication and locomotion expenditures of each node. These are subsequently used to formulate a novel nonlinear optimal control problem (OCP) over a network of autonomous nodes. It is then shown that, under certain conditions, the OCP can be transformed into an equivalent convex form. Numerical results for a single link between a node and access point allow for comparison with known solutions before the framework is applied to a multiple-node UAV network, for which previous results are not readily extended. Simulations show that transmission energy can be of the same order of magnitude as propulsion energy allowing for possible savings, whilst also exemplifying how speed adaptations together with power control may increase the network throughput.

Section: Numerical Resultsmentioning

confidence: 99%

Joint optimization of transmission and propulsion in aerial communication networks

Faqir

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

Gündüz

2017

Self Cite

“…In terms of computational complexity, the proposed formulation (13) avoids the shortcomings as listed in Remark 1. In addition, unlike the penalty-barrier finite element (PBF) method proposed in [7], which requires tailored solvers for a good performance, (13) can be efficiently solved with the same off-the-shelf sparse NLP solvers as direct collocation. The transcription and the majority of the computational components can be shared between the two, and warm starting techniques can be exploited to accelerate the computations.…”

Section: Methods Of Integrated Residual Minimizationmentioning

confidence: 99%

“…The angle of attack α is the actual control input to the physical system (aircraft); however, in order to implement Solution to the aircraft go-around in the windshear problem, integrated residual minimization method for solution representation, direct collocation with Hermite-Simpson discretization, 15 mesh intervals a constraint on its rate of change, ν is introduced as angle of attack rate and serves as the control input withα(t) = ν(t). This implementation is known to exhibit singular arc behaviour [4], [13], [14], leading to fluctuations and ringing phenomena in the solutions.…”

Section: B Aircraft Go-around In the Presence Of Windshearmentioning

confidence: 99%

See 1 more Smart Citation

Efficient and More Accurate Representation of Solution Trajectories in Numerical Optimal Control

Nie

IEEE Control Syst. Lett.

2020

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We show via examples that, when solving optimal control problems, representing the optimal state and input trajectory directly using interpolation schemes may not be the best choice. Due to the lack of considerations for solution trajectories in-between collocation points, large errors may occur, posing risks if this solution is to be applied. A novel solution representation method is proposed, capable of yielding a solution of much higher accuracy for the same discretization mesh. This is achieved by minimizing the integral of the residual error for the overall trajectory, instead of forcing the errors to be zero only at collocation points. In this way, the requirement for mesh resolution can be significantly reduced, leaving the problem dimensions relatively small. This particular formulation also avoids some of the drawbacks found in the earlier work of integrated residual minimization, leading to more efficient computations.

“…In our previous work, we proposed implementing rate constraints directly on the discretization mesh with linear constraints [6]. In this paper, we present a more in-depth analysis of this method.…”

Section: Introductionmentioning

confidence: 99%

Efficient Implementation of Rate Constraints for Nonlinear Optimal Control

Nie

2018 UKACC 12th International Conference on Control (CONTROL)

2018

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We propose a general approach to directly implement rate constraints on the discretization mesh for all collocation methods, for both state and input variables. Unlike conventional approaches that may lead to singular control arcs, the solution of this on-mesh implementation has better properties. Moreover, computational speedups of more than 30% can be achieved by exploiting the properties of the resulting linear constraint equations.