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

ENERGIES 2021年第19期14卷 6008-6008页

作者： Montani, Margherita Ronchi, Leandro Capitani, Renzo Annicchiarico, Claudio Univ Florence Dept Ind Engn Florence Via Santa Marta 3 I-50139 Florence Italy Meccan 42 Srl Via Ezio Tarantelli 15 I-50019 Florence Italy

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 reduc

关键词： autonomous driving trajectory planning path tracking sequential convex programming linear programming quadratic constraints autonomous racing car

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Robust Control Optimization for Quantum Approximate Optimization Algorithms 21st

Robust Control Optimization for Quantum Approximate Optimiza...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Dong, Yulong Meng, Xiang Lin, Lin Kosut, Robert Whaley, K. Birgitta Berkeley Ctr Quantum Informat & Computat Berkeley CA 94720 USA Univ Calif Berkeley Dept Chem Berkeley CA 94720 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA SC Solut Sunnyvale CA 94085 USA

Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation. Copyright (C) 2020 The Authors.

关键词： quantum approximate optimization algorithm robust control sequential convex programming error mitigation

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Optimal trajectories and normal load analysis of hypersonic glide vehicles via convex optimization

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AEROSPACE SCIENCE AND TECHNOLOGY 2019年第Apr.期87卷 357-368页

作者： Wang, Zhenbo Univ Tennessee Mech Aerosp & Biomed Engn Dept Knoxville TN 37996 USA

Hypersonic trajectory optimization has been intensively investigated through different approaches;however, the normal-load-optimal entry problems were barely studied and reported in the literature. Finding the optimal trajectories with maximum or minimum peak normal load is essential to evaluate the maneuverability and structural strength of the vehicle. In this paper, both the maximum and minimum peak-normal-load entry trajectories are explored using convex optimization. Based on the previous work, the maximum-peak-normal-load entry problem is firstly addressed by a Big-M method and a line-search approach. Through successive relaxations, the nonconvex discrete-event optimal control problem associated with maximum-peak-normal-load entry is transformed into a sequence of mixed-integer convex optimization problems. Then, a line-search technique is introduced to improve the convergence of the proposed method. Additionally, a sequential convex programming method is designed to solve the minimum-peak-normal-load entry problem to comprehensively analyze the normal load during the entry flight. There are efficient solvers that can solve each relaxed convex subproblem with a global optimum if the feasible set of the subproblem is nonempty. The convergence and accuracy of the proposed methodologies are demonstrated by numerical simulations, and the feasibility of the converged solutions is discussed based on an entry-corridor approach. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Hypersonic vehicles Trajectory optimization convex optimization Mixed-integer programming sequential convex programming

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Matrix Structure Driven Interior Point Method for Quadrotor Real-Time Trajectory Planning

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IEEE ACCESS 2019年 7卷 90941-90953页

作者： Xu, Guangtong Long, Teng Wang, Zhu Cao, Yan Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Minist Educ Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China

sequential convex programming (SCP) has been recently employed in various trajectory planning problems, including entry flight, planetary landing, and aircraft formation. In SCP, convex programming subproblems are sequentially solved to obtain the optimum of original nonconvex problems. For SCP-based quadrotor trajectory planning, this paper proposes a matrix-structure-driven interior point method (MSD-IPM) to improve the efficiency of solving search directions in convex programming. In MSD-IPM, primal-dual systems for solving search directions are derived from the Karush-Kuhn-Tucher (KKT) conditions of quadrotor trajectory planning subproblems. Then, the successive elimination technique is used to solve the inverse of large-scale coefficient matrices of primal-dual systems by more efficient operations on small-scale matrices. In successive elimination, the positive definiteness of several small-scale matrices is used to enhance the numerical stability of computing search directions, and the specific diagonal structures of small-scale matrices are exploited to efficiently compute the search directions. The complexity analysis shows that the efficiency of the proposed method is about one order of magnitude higher than that of the standard IPM. The comparative studies on simulation experiments demonstrate that the MSD-IPM generally outperforms several well-known optimizers (e.g., MOSEK, SDPT3, and SeDuMi) in terms of efficiency and robustness. Finally, the indoor trajectory tracking experiments indicate that the proposed method can generate smooth trajectories for real-world applications.

关键词： Quadrotor trajectory planning sequential convex programming interior point method matrix structure exploitation

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Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

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IEEE ACCESS 2020年 8卷 200129-200145页

作者： Ali, Zuhura J. Noordin, Nor K. Sali, Aduwati Hashim, Fazirulhisyam Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Wireless & Photon Networks Res Ctr Excellence WiP Serdang 43400 Malaysia

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the sequential convex programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

关键词： Greedy algorithm heterogeneous network non-orthogonal multiple access power allocation sequential convex programming

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Energy-efficient Power Allocation based on worst-case performance optimization under channel uncertainties

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2020年第11期14卷 4595-4610页

作者： Song, Xin Dong, Li Huang, Xue Qin, Lei Han, Xiuwei Northeastern Univ Shenyang 110819 Peoples R China

In the practical communication environment, the accurate channel state information (CSI) is difficult to obtain, which will cause the mismatch of resource and degrade the system performance. In this paper, to account for the channel uncertainties, a robust power allocation scheme for a downlink Non-orthogonal multiple access (NOMA) heterogeneous network (HetNet) is designed to maximize energy efficiency (EE), which can ensure the quality of service (QoS) of users. We conduct the robust optimization model based on worse-case method, in which the channel gains belong to certain ellipsoid sets. To solve the non-convex non-liner optimization , we transform the optimization problem via Dinkelbach method and sequential convex programming, and the power allocation of small cell users (SCUs) is achieved by Lagrange dual approach. Finally, we analysis the convergence performance of proposed scheme. The simulation results demonstrate that the proposed algorithm can improve total EE of SCUs, and has a fast convergence performance.

关键词： Non-orthogonal multiple access technology heterogeneous networks energy efficiency channel state information robust power allocation Dinkelbach method sequential convex programming Lagrange dual approach

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Regularized System Identification: A Hierarchical Bayesian Approach ⁎

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IFAC-PapersOnLine 2020年第2期53卷 406-411页

作者： Mohammad Khosravi Andrea Iannelli Mingzhou Yin Anilkumar Parsi Roy S. Smith Automatic Control Laboratory ETH Zürich 8092 Switzerland

In this paper, the hierarchical Bayesian method for regularized system identification is introduced. To this end, a hyperprior distribution is considered for the regularization matrix and then, the impulse response and the regularization matrix are jointly estimated based on a maximum a posteriori (MAP) approach. Toward introducing a suitable hyperprior, we decompose the regularization matrix using Cholesky decomposition and reduce the estimation problem to the cone of upper triangular matrices with positive diagonal entries. Following this, the hyperprior is introduced on a designed sub-cone of this set. The method differs from the current trend in regularized system identification from various aspect, e.g., the estimation is performed by solving a single stage problem. The MAP estimation problem reduces to a multi-convex optimization problem and a sequential convex programming algorithm is introduced for solving this problem. Consequently, the proposed method is a computationally efficient strategy specially when the regularization matrix has a large size. The method is numerically verified on benchmark examples. Owing to the employed full Bayesian approach, the estimation method shows a satisfactory bias-variance trade-off.

关键词： System identification hierarchical Bayesian sequential convex programming

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Robust Control Optimization for Quantum Approximate Optimization Algorithms

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IFAC-PapersOnLine 2020年第2期53卷 242-249页

作者： Yulong Dong Xiang Meng Lin Lin Robert Kosut K. Birgitta Whaley Berkeley Center for Quantum Information and Computation Berkeley California 94720 USA Department of Chemistry University of California Berkeley California 94720 USA Department of Mathematics University of California Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA SC Solutions Sunnyvale California 94085 USA

关键词： quantum approximate optimization algorithm robust control sequential convex programming error mitigation

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Robust sparse array design for adaptive beamforming against DOA mismatch

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SIGNAL PROCESSING 2018年 146卷 41-49页

作者： Wang, Xiangrong Amin, Moeness Wang, Xianghua Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Villanova Univ Ctr Adv Commun Villanova PA 19085 USA Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

The performance of an adaptive beamformer is significantly influenced by its array configuration. The problem of optimum array configuration for minimum variance distortionless response (MVDR) beam formers has been recently investigated under the assumption of accurate estimate or prior exact knowledge of the source direction of arrival (DOA). Inaccuracies in DOA can lead to significant performance degradation. Improving the robustness of MVDR beamformers has commonly been achieved by adding appropriate constraints in the determination of beamforming weights, such as the linearly constrained minimum variance (LCMV) beamformer. This work examines the sensitivity of different sparse array configurations towards uncertainty in the source DOA. It proposes enhancing system robustness through optimizing array configurations. The sparse array design problem is formulated in terms of maximizing the output signal-to-interference-plus-noise ratio (SINR) of the MVDR and LCMV beamformers. The constrained maximization problem is expressed as the fraction of matrix determinants, and a sequential convex programming algorithm is adopted for the solution of the corresponding non-convex problem. Numerical examples are presented to validate the robustness of configured sparse array MVDR and LCMV beamformers for small errors in source directional angles. (C) 2017 Elsevier B.V. All rights reserved.

关键词： MVDR Beamforming LCMV Beamforming Robust sparse array Angle uncertainty sequential convex programming

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An optimal operation strategy for an active power filter using cascaded H-bridges in delta-connection

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 175卷 105918-000页

作者： Wang, Hengyi Liu, Steven Univ Kaiserslautern Inst Control Syst D-67663 Kaiserslautern Germany

An optimal strategy is presented to determine the combined control reference for the terminal and circulating currents of a shunt active power filter (APF) based on the delta-connected cascaded H-bridge (CHB) topology, hereby minimizing the operational power under ideal and non-ideal grid voltages, where the ideal grid voltages contain only the positive-sequence fundamental frequency component and the non-ideal grid voltages contain unbalance and/or harmonic components. The optimization is carried out to simultaneously satisfy the desired grid current distortion limits, the source current imbalance characteristics and the power factor as well as power balance requirement. By properly coordinating terminal current control and circulating current injection of the delta-connected CHB and taking possibly unbalanced and/or distorted grids the total power rating of the APF can be kept at minimum, leading to an economical operation. The optimization problem is formulated using a convex quadratic objective function and non-convex quadratic constraints and solved by an iterative procedure. The simulation results show that the proposed strategy is applicable and effective for reactive power and harmonic compensation with an optimal rating of the APF under ideal and non-ideal grid conditions.

关键词： Optimal operation strategy sequential convex programming Active power filter Delta-connected cascaded H-bridge multilevel converter Non-ideal point of common coupling (PCC) voltages

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