Feedback utilization control in distributed real-time systems with end-to-end tasks

An increasing number of distributed real-time systems face the critical challenge of providing quality of service guarantees in open and unpredictable environments. In particular, such systems often need to enforce utilization bounds on multiple processors in order to avoid overload and meet end-to-end deadlines even when task execution times are unpredictable. While recent feedback control real-time scheduling algorithms have shown promise, they cannot handle the common end-to-end task model where each task is comprised of a chain of subtasks distributed on multiple processors. This paper presents the end-to-end utilization control (EUCON) algorithm that adaptively maintains desired CPU utilization through performance feedbacks loops. EUCON is based on a model predictive control approach that models utilization control on a distributed platform as a multivariable constrained optimization problem. A multi-input-multi-output model predictive controller is designed based on a difference equation model that describes the dynamic behavior of distributed real-time systems. Both control theoretic analysis and simulations demonstrate that EUCON can provide robust utilization guarantees when task execution times deviate from estimation or vary significantly at runtime.     

基于核密度估计的软实时任务QoS控制

针对软实时任务的服务质量（QoS）控制问题, 提出一种采用核密度估计预测控制的资源自适应调整方法。该方法首先结合资源预留策略建立软实时任务的服务质量模型, 并采用核密度估计方法, 在无法预知和假设具体作业执行时间分布的前提下通过滑动窗口样本预测当前作业的执行时间概率分布, 反馈控制器基于预测值和上一个作业完成时获得的服务质量动态调整资源以保证当前作业服务质量。仿真实验结果表明, 采用核密度估计的预测控制能有效保证任务服务质量, 在大的系统扰动下能稳定收敛。     

基于ALQ方法的飞行姿态控制系统设计

研究飞机稳定性控制优化问题,由于飞行高度和环境的变化,系统控制器性能不能满足系统的要求。为了克服常规最优控制中模型参数和外界干扰对控制器性能的影响,提出了一种应用自适应线性二次型(Adaptive Linear Quadratic,ALQ)方法的飞机纵向控制律设计技术,首先通过自适应机制实时辨识控制系统参数,辨识的参数应用于最优线性二次型的建模设计控制中,通过在线自适应的调整控制律参数,达到了理想的控制效果,仿真验证表明在存在外部扰动和建模误差时,改进算法比传统的LQ方法具有更好的鲁棒性和稳定性,可为优化设计提供参考。     

强抗扰恒值系统的逆LQ设计

本文着重讨论标量恒值系统在物理约束下的动态抗扰控制问题,提出一种能有效提高系 统动态抗扰能力的控制装置--瞬态补偿器;介绍了一种基于LQ最优控制逆问题的强抗扰 (指在一类外扰作用下动态误差小,且具有输出调节性质)控制器设计方法.通过对一调速系 统的实例研究,表明这种控制器可以大幅度地改善动态速降;无静差且稳定性充裕;物理实验 结果满意.     

Receding Horizon Control for a Class of Discrete-Event Systems With Real-Time Constraints

We consider discrete-event systems (DES) involving the control of tasks with real-time constraints. When future event time information is limited, we propose a receding horizon (RH) controller in which only some future information is available within a time window. Analyzing sample paths obtained under this scheme and comparing them to optimal sample paths (obtained when all event times are known), we derive a number of attractive properties of the RH controller, including: the fact that it still guarantees all real-time constraints; there are segments of its sample path over which all controls are still optimal; the error relative to the optimal task departure times is decreasing under certain conditions. Simulation results are included to verify the properties of the controller and show that its performance can be near-optimal even if the RH window size is relatively small     

Design of a Robust Optimal Decentralized PI Controller Based on Nonlinear Constraint Optimization For Level Regulation: An Experimental Study

This paper presents the development of a new robust optimal decentralized PI controller based on nonlinear optimization for liquid level control in a coupled tank system. The proposed controller maximizes the closed-loop bandwidth for specified gain and phase margins, with constraints on the overshoot ratio to achieve both closed-loop performance and robustness. In the proposed work, a frequency response fitting model reduction technique is initially employed to obtain a first order plus dead time (FOPDT) model of each higher order subsystem. Furthermore, based on the reduced order model, a proposed controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed optimal robust decentralized control scheme has been compared with that of a decentralized PI controller. The proposed controller is implemented in real-time on a coupled tank system. From the obtained results, it is shown that the proposed optimal decentralized PI controller exhibits superior control performance to maintain the desired level, for both the nominal as well as the perturbed case as compared to a decentralized PI controller.      

Nonlinear stochastic receding horizon control: stability,robustness and Monte Carlo methods for control approximation

This work considers the stability of nonlinear stochastic receding horizon control when the optimal controller is only computed approximately. A number of general classes of controller approximation error are analysed including deterministic and probabilistic errors and even controller sample and hold errors. In each case, it is shown that the controller approximation errors do not accumulate (even over an infinite time frame) and the process converges exponentially fast to a small neighbourhood of the origin. In addition to this analysis, an approximation method for receding horizon optimal control is proposed based on Monte Carlo simulation. This method is derived via the Feynman–Kac formula which gives a stochastic interpretation for the solution of a Hamilton–Jacobi–Bellman equation associated with the true optimal controller. It is shown, and it is a prime motivation for this study, that this particular controller approximation method practically stabilises the underlying nonlinear process.     

程序最坏执行时间极值统计方法

程序的最坏执行时间WCET是实时系统时间操作方面的可信基础,现有的WCET静态分析方法都需要对系统某种程度上的额外知识和限定性假设,导致现有的WCET分析方法本质上为偏高估计,降低了资源的利用率和系统的性能。给出一种基于极值统计的程序最坏执行时间估计新方法,采用程序执行时间的测量值作为样本,利用Gumbel分布建立程序最坏执行时间统计模型,根据测量样本序列预测执行时间的最大值,与以往的方法相比,这种方法综合体现了各种硬件特性对程序执行时间的影响,估计结果更为精确,更适合处理硬件特性和软件复杂度较高情况下的程序最坏执行时间估计。实验结果表明利用Gumbel分布建立的WCET估计模型能够快速且有效地给出实时程序的最坏执行时间估计。     

Trade-Off Analysis of Real-Time Control Performance and Schedulability*

Most real-time computer-controlled systems are developed in two separate stages: controller design followed by its digital implementation. Computational tasks that implement the control algorithms are usually scheduled by treating their execution times and periods as unchangeable parameters. Task schedulability therefore depends only on the limited computing resources available. On the other hand, controller design is primarily based on the continuous-time dynamics of the physical system being controlled. The set of tasks resulting from this controller design may not be schedulable with the limited computing resources available. Even if the given set of tasks is schedulable, their overall performance may not be optimal in the sense that they do not make a full use of the computing resources. In this paper, we propose an integrated approach to controller design and task scheduling. Specifically, task frequencies (or periods) are allowed to vary within a certain range as long as such changes do not affect critical control functions such as the maintenance of system stability. We present an algorithm that determines the task frequencies such that a prescribed aspect of system performance is optimized subject to satisfaction of computing resource constraints. The tasks are then scheduled with the chosen frequencies. The proposed approach also addresses the issue of choosing controller processors.     

Optimal scheduling of a communication channel for the centralized control of a platoon of vehicles

This paper proposes an off-line optimal channel scheduling algorithm for an interconnected vehicle control system. The optimal sequence obtained through the scheduling algorithm provides a switching controller with the best switching order if the controller can access only one plant at each time slot over the shared communication medium. Interconnected systems require the string stability as well as the dynamic stability of each unit. This paper shows that integrating the simple string stable control law with the approximately optimal linear-quadratic (LQ) tracker gives the optimal channel scheduling algorithm.     

一类非线性欠驱动系统的混沌优化LQ控制

提出了一种基于混沌优化线性二次最优控制器权矩阵参数的三级倒立摆控制方法;根据系统控制的目标,设计了一类适合多变量系统的优化性能指标函数;这类性能指标函数综合考虑三级倒立摆系统各个输出间的重要程度,以及动态特性和稳定性要求,结合文中的性能指标函数,首先利用混沌粗搜索得到控制器权矩阵参数的次优解,再在次优解的邻域内继续寻优,得到全局最优的权矩阵参数;利用这种方法得到的LQ控制器,有效地实现了对三级倒立摆的稳定控制。     

A unified method for evaluating real-time computer controllers and its application

A computer-controlled system is a synergistic coupling of the controlled process and the controller computer. We have defined new performance measures for real-time controller computers based on this coupling. We present a systematic study of a typical critical controlled process in the context of new performance measures that express the performance of both controlled processes and controller computers (taken as a unit) on the basis of a single variable: controller response time. Controller response time is a function of current system state, system failure rate, electrical and/or magnetic interference, etc., and is therefore a random variable. Control overhead is expressed as monotonically nondecreasing function of the response time and the system suffers catastrophic failure, or dynamic failure, if the response time for a control task exceeds the corresponding system hard deadline, if any. The controlled-process chosen for study is an aircraft in the final stages of descent, just prior to landing. Control constraints are particularly severe during this period, and great care must be taken in the design of controllers that handle this process. First, the performance measures for the controller are presented. Second, control algorithms for solving the landing problem are discussed, and finally the impact of our performance measures on the problem is analyzed, showing that the performance measures and the associated estimation method have potential use for designing and/or evaluating real-time controllers and controlled process. In common with all other control techniques, the computational complexity involved in obtaining these measures is susceptible to the curse of dimensionality.     

Adaptive nonlinear information fusion preview control for autonomous surface vessels subject to measurement noises and unknown input saturations

This study presents an adaptive nonlinear information fusion preview control (NIFPC) method for trajectory tracking of autonomous surface vessels (ASVs) subject to system uncertainty, measurement noise, and unknown input saturations. The NIFPC is developed based on the nonlinear information fusion estimation methodology, in which the system's future reference trajectory information, noise information, performance index requirements, and system dynamic model are all transformed into information equations related to control input, and then the current control action is obtained by fusing these previewed future information via the nonlinear information fusion optimal estimation. In order to avoid the unknown input saturation constraints, a fuzzy asymmetric saturated approximator (FASA) is designed and integrated into the controller, where the fuzzy logic system (FLS) is used to adaptively adjust the key boundary parameters of the approximator. As a result, the negative effects caused by system uncertainty and measurement noise can be effectively suppressed, while the completely unknown input saturation constraints in the system actuator are guaranteed not to be violated. The convergence of the tracking errors of the closed-loop system is guaranteed via Lyapunov stability theory. Numerical simulation results have been provided to demonstrate the satisfactory performance of the proposed control scheme.     

Mini-max integral sliding-mode control for multimodel linear uncertain systems

An original linear time-varying system with matched and unmatched disturbances and uncertainties is replaced by a finite set of dynamic models such that each one describes a particular uncertain case including exact realizations of possible dynamic equations as well as external unmatched bounded disturbances. Such a tradeoff between an original uncertain linear time varying dynamic system and a corresponding higher order multimodel system containing only matched uncertainties leads to a linear multi-model system with known unmatched bounded disturbances and unknown matched disturbances as well. Each model from a given finite set is characterized by a quadratic performance index. The developed minimax integral sliding mode control strategy gives an optimal minimax linear quadratic (LQ)-control with additional integral sliding mode term. The design of this controller is reduced to a solution of an equivalent mini-max LQ problem that corresponds to the weighted performance indices with weights from a finite dimensional simplex. The additional integral sliding mode controller part completely dismisses the influence of matched uncertainties from the initial time instant. Two numerical examples illustrate this study.     

Multisensor fusion fault tolerant control

In this paper, a multisensor fusion fault tolerant control system with fault detection and identification via set separation is presented. The fault detection and identification unit verifies that for each sensor–estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and performance under the occurrence of abrupt sensor faults. Experimental validation, illustrating the multisensor fusion fault tolerant control strategy is included.     

Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints.

A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the "strategic" utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysis.     

通讯信息约束下具有全局稳定性的分布式系统预测控制

本文针对一类由状态相互耦合的子系统组成的分布式系统, 提出了一种可以处理输入约束的保证稳定性的非 迭代协调分布式预测控制方法(distributed model predictive control, DMPC). 该方法中, 每个控制器在求解控制率时只与 其它控制器通信一次来满足系统对通信负荷限制; 同时, 通过优化全局性能指标来提高优化性能. 另外, 该方法在优化 问题中加入了一致性约束来限制关联子系统的估计状态与当前时刻更新的状态之间的偏差, 进而保证各子系统优化问 题初始可行时, 后续时刻相继可行. 在此基础上, 通过加入终端约束来保证闭环系统渐进稳定. 该方法能够在使用较少 的通信和计算负荷情况下, 提高系统优化性能. 即使对于强耦合系统同样能够保证优化问题的递推可行性和闭环系统的 渐进稳定性. 仿真结果验证了本文所提出方法的有效性.     

Stability and operating constraints of adaptive LMS-based feedback control

The filtered-X LMS algorithm has enjoyed widespread usage in both adaptive feedforward and feedback controller architectures. For feedforward controller designs the filtered-X LMS algorithm has been shown to exhibit unstable divergence for plant estimation errors in excess of ±90°. Typical implementations of this algorithm in adaptive feedback controllers such as filtered-U and filtered-E have previously been assumed to conform to these same identification constraints. Here we present two instability mechanisms that can arise in filtered-E control that violate the 90° error assumption: feedback loop instabilities and LMS algorithm divergence. Analysis of the adaptive feedback system indicates that the conventionally interpreted plant estimation error can be arbitrarily small yet induce algorithm divergence; while other cases may have very large estimation errors and feedback loops cause controller instability. These analytical observations are supported by simulations. The implications of the actual plant estimation error, calculated here for the filtered-E controller, are extended to practical constraints placed on applications including filtered-U, on-line system identification, and self-excited system control.     

基于内模扩展LQ方法的WMR轨迹跟踪控制

针对现有非线性控制方案的一些瓶颈问题,从线性控制的角度出发,开展了一种用于WMR的线性二次型最优控制方法设计的研究.首先,基于WMR的运动学模型采用动态反馈线性化技术将非线性运动学模型转化为线性模型;然后,选取跟踪误差及误差收敛速度作为设计指标;同时考虑实现渐进跟踪,针对不同形式的参考轨迹,根据内模原理对控制器模态进行扩展,利用线性模型设计基于内模扩展LQ最优轨迹跟踪控制器;最后通过动态反馈反变换得到实际控制器.此外,通过将此方法的控制效果与几种经典方法进行仿真比对,说明了此方法对于跟踪的精确性和快速性上有较大优势.     

线性离散奇异系统的不定二次最优控制问题: Krein空间方法

The finite time horizon indefinite linear quadratic(LQ) optimal control problem for singular linear discrete time-varying systems is discussed. Indefinite LQ optimal control problem for singular systems can be transformed to that for standard state-space systems under a reasonable assumption. It is shown that the indefinite LQ optimal control problem is dual to that of projection for backward stochastic systems. Thus, the optimal LQ controller can be obtained by computing the gain matrices of Kalman filter. Necessary and sufficient conditions guaranteeing a unique solution for the indefinite LQ problem are given. An explicit solution for the problem is obtained in terms of the solution of Riccati difference equations.