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2020 American Control Conference (ACC)最新文献

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Piecewise-Linear Path Following for a Unicycle using Transverse Feedback Linearization 基于横向反馈线性化的单轮车分段线性路径跟踪
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147473
Rollen S. D’Souza, Christopher Nielsen
Path planning and following together constitute a critical part of the decision-making hierarchy in autonomous ground vehicles. One of the simplest instances of this architecture is when the path planner generates waypoints that define a sequence of collision free line segments from a start location to goal destination and when the vehicle’s kinematic model is taken to be Dubin’s vehicle. The low level feedback controller can then be design by treating the path following problem as a set stabilization problem; one such approach is called transverse feedback linearization (TFL). However, for a Dubin’s vehicle with only one input, the direction of traversal along the path is completely determined by the vehicle’s initial condition. In this paper we provide easily certifiable sufficient conditions and a systematic design procedure that guarantees the robot satisfies the initial condition requirements at transitions between line segments of the path. Our analysis relies on geometric properties of the path; as a result we construct a formal connection between the feasible motions generated by the planner and the path following controller’s convergence properties.
路径规划和跟随共同构成了自主地面车辆决策层次的关键部分。这种架构的一个最简单的例子是,当路径规划器生成路径点时,路径点定义了从起点到目标目的地的一系列无碰撞线段,并且当车辆的运动学模型被取为Dubin的车辆时。通过将路径跟踪问题作为集稳定问题来设计低电平反馈控制器;其中一种方法叫做横向反馈线性化(TFL)。然而,对于只有一个输入的杜宾车辆,沿着路径的穿越方向完全由车辆的初始条件决定。在本文中,我们提供了易于证明的充分条件和系统的设计程序,以保证机器人在路径线段之间的过渡满足初始条件要求。我们的分析依赖于路径的几何特性;因此,我们在规划器生成的可行运动与遵循控制器收敛性的路径之间建立了形式化的联系。
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引用次数: 2
Nonlinear Model Predictive Control Using Output Feedback 基于输出反馈的非线性模型预测控制
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147908
Mathis Allen, H. Khalil
This paper presents an output feedback model predictive control for a class of nonlinear systems in a multirate scheme, where the control sampling period is larger than the estimation sampling period. With a small sampling period, the observer is designed to be faster than the dynamics of the closed-loop system under state feedback. Stabilization is achieved by a separation approach in which the control is designed first using state feedback and practical stabilization is achieved by output feedback.
针对一类非线性系统,提出了一种控制采样周期大于估计采样周期的多速率输出反馈模型预测控制方法。在采样周期小的情况下,观测器被设计得比状态反馈下闭环系统的动力学更快。稳定是通过分离方法实现的,其中控制首先使用状态反馈设计,实际稳定是通过输出反馈实现的。
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引用次数: 0
Attack detection of nonlinear distributed control systems 非线性分布式控制系统的攻击检测
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147765
Xu Zhang, Yang Lu, Minghui Zhu
This paper considers a class of nonlinear distributed control systems subject to false data injection attacks, Byzantine attacks and switching attacks. The problem of attack detection is formulated as the simultaneous recovery of system states, attack vectors and system mode of a switched nonlinear system. In the proposed attack detection algorithm, the inverse system of each mode aims to estimate system states and attack vectors when the corresponding mode is input-output decoupled. A set-valued mode index map gives all modes which generate a switch-singular pair. A machine learning example is used to validate the performance of the developed algorithm.
本文研究了一类具有虚假数据注入攻击、拜占庭攻击和切换攻击的非线性分布式控制系统。将攻击检测问题表述为一个切换非线性系统的系统状态、攻击向量和系统模式的同时恢复问题。在本文提出的攻击检测算法中,每种模式的逆系统旨在估计输入输出解耦模式下的系统状态和攻击向量。集值模式索引映射给出了产生交换奇异对的所有模式。通过一个机器学习实例验证了所开发算法的性能。
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引用次数: 0
Optimal Operation of a Hybrid Hydraulic Electric Architecture (HHEA) for Off-Road Vehicles Over Discrete Operating Decisions 基于离散操作决策的越野车混合动力系统的最优运行
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147839
J. Siefert, Perry Y. Li
Most off-highway construction and agriculture equipment use hydraulics, which has unmatched power density, for power transmission and throttling as a means for control. Trends towards better efficiency and electrification motivated a novel Hybrid Hydraulic-Electric Architecture (HHEA) which could significantly reduce energy consumption even in high power machines that would be too costly to electrify directly. This is achieved by using a set of common pressure rails to transmit the majority of power hydraulically and modulating the power with small electric motor-drives to achieve precise control. This paper proposes a computationally efficient, Lagrange multiplier method for computing the optimal sequence of pressure rail selections to minimize energy use. This is needed to evaluate HHEA’s energy saving potential and for iterative architecture design and sizing. An interesting complication is that the cost function is not fully defined until the candidate control sequence is fully specified. This issue is dealt with by decomposing the original problem into a set of subproblems with inequality constraints that can be solved efficiently. A case study of an off-road construction machine demonstrates that the HHEA reduces energy consumption by 2/3 compared to the baseline load sensing architecture.
大多数非公路建筑和农业设备使用液压,它具有无与伦比的功率密度,用于动力传输和节流作为控制手段。提高效率和电气化的趋势推动了一种新型的混合动力液压-电动结构(HHEA)的发展,这种结构可以显著降低能耗,即使是在高功率机器上,这种机器的成本太高,无法直接电气化。这是通过使用一组通用压力导轨来液压传输大部分动力,并通过小型电动机驱动器调节功率来实现精确控制。本文提出了一种计算效率高的拉格朗日乘数法,用于计算压力轨选择的最优顺序,以最小化能源消耗。这需要评估HHEA的节能潜力,以及迭代的建筑设计和规模。一个有趣的复杂情况是,在候选控制序列完全指定之前,成本函数没有完全定义。这个问题是通过将原问题分解为一组具有不等式约束的子问题来解决的,这些子问题可以有效地求解。一个非公路施工机械的案例研究表明,与基线负载敏感结构相比,HHEA减少了2/3的能耗。
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引用次数: 5
An Improved Iterative Learning Control for Uncertain Multi-Axis Systems 不确定多轴系统的改进迭代学习控制
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147845
A. Armstrong, A. Alleyne
For learning control algorithms to date, the convergence rate in the iteration domain depends on the level of plant knowledge. This work presents a Fast Cross-coupled Iterative Learning Control (F-CCILC) scheme to overcome the current limitations in learning control algorithms. F-CCILC achieves fast convergence for multi-input multi-output (MIMO) systems with high model uncertainty. The approach uses involves using a novel error term in the ILC learning law based on techniques from Sliding Mode Control (SMC). The input signal is guaranteed to remain bounded in the time and iteration domains, and the approach does not require end-user tuning of arbitrary gains. In this paper, the design for the F-CCILC system is presented, and the performance of this system is compared to the performance of existing ILC control schemes via simulations and experimental testing. Compared to the current control methods, the simulation results demonstrate increased robustness and learning speeds for multi-axis systems with significant model uncertainty.
对于迄今为止的学习控制算法,迭代域的收敛速度取决于植物知识的水平。本文提出了一种快速交叉耦合迭代学习控制(F-CCILC)方案,以克服当前学习控制算法的局限性。F-CCILC对模型不确定性较大的多输入多输出(MIMO)系统实现了快速收敛。该方法在基于滑模控制(SMC)技术的ILC学习律中引入了一种新的误差项。输入信号保证在时间和迭代域中保持有界,并且该方法不需要最终用户调整任意增益。本文介绍了F-CCILC系统的设计,并通过仿真和实验测试,将该系统的性能与现有ILC控制方案的性能进行了比较。与现有的控制方法相比,仿真结果表明,对于具有显著模型不确定性的多轴系统,该方法具有更高的鲁棒性和学习速度。
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引用次数: 2
Dual, Iterative H2-Conic Controller Synthesis 对偶迭代h2 -圆锥控制器综合
Pub Date : 2020-07-01 DOI: 10.23919/acc45564.2020.9147649
Liangting Wu, L. Bridgeman
The Conic Sector Theorem can be employed for controller synthesis to ensure input-output stability. This work develops a method to synthesize conic, observer-based controllers by minimizing an upper-bound on the closed-loop ${mathcal{H}_2}$-norm. The proposed method can be seen as the dual of an existing optimal synthesis method, but with an alternative initialization to expand the set of plants for which it is feasible. This results in better performance in some examples and therefore provides a useful alternative tool for robust and optimal control.
二次扇形定理可用于控制器综合,以保证输入输出的稳定性。本文提出了一种通过最小化闭环${mathcal{H}_2}$范数的上界来合成基于观测器的二次型控制器的方法。所提出的方法可以看作是现有最优合成方法的对偶,但具有可选的初始化,以扩大可行的植物集。在某些示例中,这可以获得更好的性能,因此为鲁棒和最优控制提供了有用的替代工具。
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引用次数: 1
Exact Completion of Rectangular Matrices Using Ramanujan Bigraphs 矩形矩阵用拉马努金图的精确补全
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147379
Shantanu Prasad Burnwal, M. Vidyasagar
In this paper, we study the matrix completion problem: Suppose $X in {mathbb{R}^{{n_r} times {n_c}}}$ is unknown except for an upper bound r on its rank. By measuring a small number m ≪ nrnc of elements of X, is it possible to recover X exactly, or at least, to construct a reasonable approximation of X? At present, there are two approaches to choosing the sample set, namely probabilistic and deterministic. Probabilistic methods can guarantee exact recovery of the unknown matrix, but only with high probability. In this approach, samples are taken uniformly at random. Therefore we need to start sampling for every new matrix afresh. In the deterministic approach, sampling points can be kept fixed. At present, there are very few deterministic methods, and they mostly apply only to square matrices. In this paper, we present a deterministic method for selecting the sample set that can guarantee the exact recovery of the unknown matrix. This approach works for the recovery of rectangular as well as square matrices. We achieve this by choosing the elements to be sampled as the edge set of a Ramanujan bigraph. If samples are the edge set of a Ramanujan bigraph, then we can recover the unknown matrix from that sample set using nuclear norm minimization. A companion paper discusses the explicit construction of Ramanujan bigraphs. We provide a sufficient condition, that is if the samples taken are of the order of r3 then we can recover the unknown entries exactly if the unknown matrix satisfies some coherence condition. We believe this the first sufficient condition available using deterministic sampling technique and nuclear norm minimization.
本文研究了矩阵补全问题:假设$X In {mathbb{R}^{{n_r} 乘以{n_c}} $除了秩上有上界R外是未知的。通过测量少量的X元素的m < nrnc,是否有可能精确地得到X,或者至少构造出X的合理近似值?目前,有两种选择样本集的方法,即概率方法和确定性方法。概率方法可以保证未知矩阵的精确恢复,但只有在高概率的情况下。在这种方法中,样本是均匀随机抽取的。因此,我们需要对每个新矩阵重新开始采样。在确定性方法中,采样点可以保持固定。目前,确定性方法很少,而且大多只适用于方阵。本文提出了一种确定的样本集选择方法,保证了未知矩阵的精确恢复。这种方法既适用于矩形矩阵的恢复,也适用于方阵的恢复。我们通过选择要采样的元素作为拉马努金图的边集来实现这一点。如果样本是拉马努金图的边集,那么我们可以使用核范数最小化从该样本集恢复未知矩阵。另一篇论文讨论了拉马努金图的显式构造。我们提供了一个充分条件,如果所取的样本是r3阶的,那么如果未知矩阵满足相干性条件,我们可以准确地恢复未知项。我们认为这是采用确定性抽样技术和核范数最小化方法得到的第一个充分条件。
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引用次数: 0
Analysis of Online Parameter Estimation for Electrochemical Li-ion Battery Models via Reduced Sensitivity Equations
Pub Date : 2020-07-01 DOI: 10.23919/acc45564.2020.9147260
Z. Gima, Dylan Kato, Reinhardt Klein, S. Moura
This paper focuses on the problem of online parameter estimation in an electrochemical Li-ion battery model. Online parameter estimation is necessary to account for model mismatch, environmental disturbances, and cycle-induced aging in Li-ion battery models. Sensitivity analysis can improve parameter estimation by identifying which data the parameters are most sensitive to. However, computing parameter sensitivity in full-order electrochemical models is typically intractable for online applications. Using a reduced-order model can lower the computational burden and, as we demonstrate, approximates well the sensitivity of the higher-order model. To provide further insight into the parameter estimation challenge, we analyze the effect that identifying parameters according to voltage RMSE data has on internal state errors. We perform a simulation study which demonstrates that parameter estimation approaches based on this paradigm are not sufficient for safe battery operation or other control objectives that require accurate estimates of these states.
研究了电化学锂离子电池模型的在线参数估计问题。在锂离子电池模型中,在线参数估计是考虑模型失配、环境干扰和循环诱发老化的必要条件。灵敏度分析可以通过识别参数对哪些数据最敏感来改进参数估计。然而,计算全阶电化学模型的参数灵敏度对于在线应用来说是非常棘手的。使用降阶模型可以降低计算负担,并且如我们所示,可以很好地近似高阶模型的灵敏度。为了进一步了解参数估计的挑战,我们分析了根据电压RMSE数据识别参数对内部状态误差的影响。我们进行了一项模拟研究,该研究表明,基于这种范式的参数估计方法对于电池的安全运行或其他需要准确估计这些状态的控制目标是不够的。
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引用次数: 8
Adaptive Admittance Control of Hybrid Exoskeletons 混合外骨骼的自适应导纳控制
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147928
Christian A. Cousin
Hybrid exoskeletons combine two increasingly common rehabilitative therapies, functional electrical stimulation (FES) and robotic therapy, for use on individuals with neuromuscular disorders. As hybrid exoskeletons increase in popularity and complexity, it remains an ever-important issue to not only assist people in performing rehabilitation, but also to guarantee their safety while coupled to the exoskeleton. In this paper, a novel adaptive controller for hybrid exoskeletons is developed to regulate an admittance error system using the exoskeleton’s motors while simultaneously regulating a position error system using the operator’s muscles, stimulated through FES. The stability of the controller is rigorously analyzed using a combined Lyapunov-passivity approach and while the hybrid exoskeleton is proven to be energetically passive, the admittance error system is proven to demonstrate global exponential convergence to a uniform ultimate bound. Simulations were performed on a two degree-of-freedom lower-limb hybrid exoskeleton to demonstrate the efficacy of the controller. Results show the controller achieves an average admittance tracking error of 0.00±0.08 rad and 0.00±0.08 rad/s for joint one (the knee joint), and 0.01±0.11 rad and 0.01±0.12 rad/s for joint two (the ankle joint), while simultaneously applying FES to the operator’s muscles for rehabilitation.
混合外骨骼结合了两种越来越常见的康复疗法,功能性电刺激(FES)和机器人疗法,用于神经肌肉疾病患者。随着混合外骨骼的普及和复杂性的增加,不仅要帮助人们进行康复,还要保证他们在与外骨骼耦合时的安全,这仍然是一个越来越重要的问题。本文开发了一种新型的混合外骨骼自适应控制器,利用外骨骼的马达调节导纳误差系统,同时利用FES刺激的操作者肌肉调节位置误差系统。采用组合lyapunov - passive方法对控制器的稳定性进行了严格的分析,同时证明了混合外骨骼是能量被动的,证明了导纳误差系统具有全局指数收敛到一致的最终界。以二自由度下肢混合外骨骼为实验对象进行了仿真,验证了该控制器的有效性。结果表明,控制器对关节1(膝关节)的平均导纳跟踪误差分别为0.00±0.08 rad和0.00±0.08 rad/s,对关节2(踝关节)的平均导纳跟踪误差分别为0.01±0.11 rad和0.01±0.12 rad/s,同时对操作者肌肉施加FES进行康复。
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引用次数: 2
Iterative Optimal Control Syntheses for Nonlinear Systems in Constrained Environments 约束环境下非线性系统的迭代最优控制综合
Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147993
Minh Vu, Shen Zeng
This paper presents computational solutions for nonlinear optimal control problems with general state-space constraints that may not be representable in terms of analytical expressions. The presented approaches hinge on the iterative nature of an underlying computational nonlinear optimal control methodology by which the non-analytical constraint description can be incorporated as quadratic constraints within each iteration. The functionality and efficiency of the proposed methods are discussed from a computational point of view and illustrated on a standard parallel parking problem.
本文提出了具有一般状态空间约束的非线性最优控制问题的计算解,这些约束可能无法用解析表达式表示。所提出的方法取决于底层计算非线性最优控制方法的迭代性质,通过该方法,非解析性约束描述可以在每次迭代中作为二次约束合并。从计算的角度讨论了所提方法的功能和效率,并举例说明了一个标准的平行停车问题。
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引用次数: 5
期刊
2020 American Control Conference (ACC)
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