Time-optimal hands-off control for linear time-invariant systems

Ikeda, Takeshi; Nagahara, Masaaki

doi:10.1016/j.automatica.2018.10.004

Cited by 25 publications

(18 citation statements)

References 19 publications

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“…Beyond these three classes of problems, sparsity-inducing optimization can be a methodological ingredient in many other problems such as regressor selection, estimation in the conditions where the data sequences suffer some missing points 58 , maximum hands-off control 59 , time optimal control 60,61 , control of hybrid systems 56 , fault-tolerant control, state estimation for switched system 55 , subspace clustering 26,25 , signal recovery, image denoising, etc.…”

Section: Discussionmentioning

confidence: 99%

On sparsity‐inducing methods in system identification and state estimation

Bako

2022

Intl J Robust & Nonlinear

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The purpose of this paper is to survey some sparsity-inducing methods in system identification and state estimation. Such methods can be divided into two main categories: methods inducing sparsity in the parameters and those sparsifying the prediction error. In the last class we discuss in particular the Least Absolute Deviation (LAD) estimator and its robustness properties with respect to sparse noise in both cases of univariate and multivariate measurements. We also discuss the application of sparsity-inducing methods to switched system identification and to state estimation for linear systems in the presence sparse and dense measurement noises. While the presentation focuses essentially on bridging some existing results, some technical refinements and new features are also provided.

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Section: Discussionmentioning

confidence: 99%

On sparsity‐inducing methods in system identification and state estimation

Bako

2022

Intl J Robust & Nonlinear

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“…Maximum hands-off control [12] is sparse control that minimizes the time duration on which the control values are nonzero by minimizing the L 1 norm of the control, relaxation of L 0 norm. This idea has been extended to distributed control [13], time-optimal control [14], and discrete-valued control [15]. Also, sparse control has been proposed for controller complexity reduction [16], [17], [18], [19] by promoting sparsity of the feedback gain minimizing the ℓ 1 norm or the sum-of-logs instead of its ℓ 0 norm.…”

Section: Introductionmentioning

confidence: 99%

Iterative Greedy LMI for Sparse Control

Nagahara

Ogura

Yamämoto

2022

IEEE Control Syst. Lett.

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In this paper, we propose a novel method to find matrices that satisfy sparsity and LMI (linear matrix inequality) constraints at the same time. This problem appears in sparse control design such as sparse representation of the state feedback gain, sparse graph representation with fastest mixing, and sparse FIR (finite impulse response) filter design, to name a few. We propose an efficient algorithm for the solution based on Dykstra's projection algorithm. We then prove a convergence theorem of the proposed algorithm, and show some control examples to illustrate merits and demerits of the proposed method.

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“…It is important to reduce the parking time of an optimal control system. In Ikeda and Nagahara [25], the time spent of state transition is minimized by adding the time spent of the control to the cost function. To be more environmentally friendly and save energy, energy consumption should also be one of the important indicators.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive pseudospectral method for the optimal control problem of automatic car parking

Chen

Mai

Zhang

et al. 2021

Asian Journal of Control

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This paper proposes a novel adaptive pseudospectral (NAP) method for solving the time-energy optimal control model to minimize the parking time and the energy output of the vehicle. The time-energy optimal control model is first discretized into a nonlinear programming problem at Gauss collocations by the NAP method. Subsequently, we prove the equivalence between the nonlinear programming problem derived from the NAP method and the time-energy optimal control model problem. Compared to those of the interior point method and the piecewise gauss pseudospectal method, simulation results show that the NAP method has higher computational efficiency and accuracy in solving the time-energy optimal control model problem.

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Time-optimal hands-off control for linear time-invariant systems

Cited by 25 publications

References 19 publications

On sparsity‐inducing methods in system identification and state estimation

On sparsity‐inducing methods in system identification and state estimation

Iterative Greedy LMI for Sparse Control

A novel adaptive pseudospectral method for the optimal control problem of automatic car parking

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