不确定系统D稳定与方差约束的鲁棒H∞可靠控制

研究了一类不确定系统区域稳定和方差约束的鲁棒H∞可靠控制器的设计方法,其目的是通过设计状态反馈控制器,使闭环系统的极点被配置在给定的圆盘中,同时,当有外生扰动,以及所有可能的不确定及其敏感执行器失效的时候,依然能满足闭环传递函数H∞范数有界和稳态方差的约束.借助于LMI和凸优化技术,给出了控制器存在的充分条件及其解析式.最后,通过一个仿真例子,说明该方法的可行性.     

大规模农田传感器网络通信能耗模型

将无线传感器网络应用于农业中,构成农田监控网络,是实现农业信息化、自动化、智能化的重要手段.然而,农田无线传感器网络具有超大规模、超低成本、拓扑变化复杂等特点,这些特征对网络通信设计提出了更加苛刻的要求.从Heinzelman的无线传输能量模型出发,建立了一个适用于大规模农田传感器网络的通信能耗模型,该能量模型考虑了部署间距、传输半径以及数据包大小对系统总能耗的影响,获得了在传输间距z确定情况下,求最佳传输半径R的计算方法.同时,仿真分析了不同部署半径、不同网络规模对全网查询能耗的影响.     

A classification of methods for distributed system optimization based on formulation structure

Augmented Lagrangian coordination (ALC) is a provably convergent coordination method for multidisciplinary design optimization (MDO) that is able to treat both linking variables and linking functions (i.e. system-wide objectives and constraints). Contrary to quasi-separable problems with only linking variables, the presence of linking functions may hinder the parallel solution of subproblems and the use of the efficient alternating directions method of multipliers. We show that this unfortunate situation is not the case for MDO problems with block-separable linking constraints. We derive a centralized formulation of ALC for block-separable constraints, which does allow parallel solution of subproblems. Similarly, we derive a distributed coordination variant for which subproblems cannot be solved in parallel, but that still enables the use of the alternating direction method of multipliers. The approach can also be used for other existing MDO coordination strategies such that they can include block-separable linking constraints. This work is funded by MicroNed, grant number 10005898.     

Controller synthesis with guaranteed closed-loop phase constraints

In this paper, we present an analysis and synthesis approach for guaranteeing that the phase of a single-input, single-output closed-loop transfer function is contained in the interval [−α,α] for a given α>0 at all frequencies. Specifically, we first derive a sufficient condition involving a frequency domain inequality for guaranteeing a given phase constraint. Next, we use the Kalman–Yakubovich–Popov theorem to derive an equivalent time domain condition. In the case where , we show that frequency and time domain sufficient conditions specialize to the positivity theorem. Furthermore, using linear matrix inequalities, we develop a controller synthesis approach for guaranteeing a phase constraint on the closed-loop transfer function. Finally, we extend this synthesis approach to address mixed gain and phase constraints on the closed-loop transfer function.     

MPC for tracking piecewise constant references for constrained linear systems

In this paper, a novel model predictive control (MPC) for constrained (non-square) linear systems to track piecewise constant references is presented. This controller ensures constraint satisfaction and asymptotic evolution of the system to any target which is an admissible steady-state. Therefore, any sequence of piecewise admissible setpoints can be tracked without error. If the target steady state is not admissible, the controller steers the system to the closest admissible steady state.These objectives are achieved by: (i) adding an artificial steady state and input as decision variables, (ii) using a modified cost function to penalize the distance from the artificial to the target steady state (iii) considering an extended terminal constraint based on the notion of invariant set for tracking. The control law is derived from the solution of a single quadratic programming problem which is feasible for any target. Furthermore, the proposed controller provides a larger domain of attraction (for a given control horizon) than the standard MPC and can be explicitly computed by means of multiparametric programming tools. On the other hand, the extra degrees of freedom added to the MPC may cause a loss of optimality that can be arbitrarily reduced by an appropriate weighting of the offset cost term.     

基于随机模型的艾滋病预防与控制策略研究

建立了艾滋病传播过程中感染者人数占总人口比例所满足的随机模型,分析了环境因素对艾滋病传播过程的影响,利用数值解方法对模型进行分析,结合我国数十年间艾滋病病毒感染者实际数据进行仿真,验证模型的有效性;并对未来我国艾滋病病毒感染者人数占总人口比例进行预测,通过对其中的传染率控制指标β进行分析,得出这一指标的波动对艾滋病流行趋势的影响.通过分析其均值与方差,提出对β的调整策略以达到一定的控制目标,从而为艾滋病的预防和控制提供一定程度上的决策支持.     

Adaptive neural network dynamic event-triggered control for strong interconnected stochastic nonlinear systems with output constraint

This paper proposes a dynamic event-triggered mechanism based command filtered adaptive neural network (NN) tracking control scheme for strong interconnected stochastic nonlinear systems with time-varying output constraints. By designing a state observer, the unmeasured states of the systems can be estimated. The NNs are utilized to handle the unknown intermediate functions. In the controller design process, the asymmetric time-varying barrier Lyapunov functions are used to guarantee that the systems outputs do not violate the constraint regions. By integrating the command filter with variable separation technique, the controller design process is more simple, and the problem of algebraic-loop can be solved which caused by interconnected functions. According to the Lyapunov stability theory, it can be ensured that all signals of the systems are bounded in probability. Finally, the availability of the developed control scheme can be showed by the simulation example.     

Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control

The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle.     

Control allocation technology based on fault diagnosis for the unmanned aerial vehicle system subject to physical constraints and fault reconfiguration mismatch

The problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four-rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo-inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.     

Event-triggered-based integral reinforcement learning output feedback optimal control for partially unknown constrained-input nonlinear systems

In this paper, an adaptive output feedback event-triggered optimal control algorithm is proposed for partially unknown constrained-input continuous-time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event-triggered condition is established, and only when the event-triggered condition is violated will the event be triggered and the state be sampled. Then, an event-triggered-based synchronous integral reinforcement learning (ET-SIRL) control algorithm with critic-actor neural networks (NNs) architecture is proposed to solve the event-triggered Hamilton–Jacobi–Bellman equation under the established event-triggered condition. The critic and actor NNs are used to approximate cost function and optimal event-triggered optimal control law, respectively. Meanwhile, the event-triggered-based closed-loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET-SIRL control algorithm.