实时控制系统协同设计方法及应用研究
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摘要
实时控制系统(RTCS)通常是指在实时操作系统(RTOS)上执行多个控制任务,在有限处理器资源上实现竞争性使用。实时控制系统协同设计是将控制理论与调度策略相结合,在设计控制器同时考虑调度引起的固有抖动特性对性能指标的影响,保证控制对象允许运行的性能指标。另一方面,在调度算法的研究过程中,也应考虑控制器设计参数约束,在提高任务的可调度性同时提高控制系统性能。本文以提高有限资源下实时系统控制性能为目的,对实时控制系统协同设计方法及应用进行了深入的研究,论文主要工作及创新点有：
1.分析研究实时控制系统中控制和调度之间的相互影响,为系统协同设计提供支持。控制对调度的影响主要是指不同的处理机负载或不同的控制性能要求都会影响实时系统中任务的可调度性能。反之,调度引起的采样抖动和输入输出时延抖动也会在不同程度上影响控制系统性能。
2.针对控制任务的不确定性引起的实时系统负载变化,提出一种自适应反馈弹性周期模型处理实时系统超载,通过检测任务作业实际执行时间,计算处理机利用率预估值,在线自适应调节任务周期来适应动态变化的系统负载,提高实时控制系统的可调度性,保证控制对象性能。仿真实例表明,该协同设计模型有效实用,并能以更灵活的方式处理实时系统超载。
3.为保证实时控制系统中关键任务的可调度性并提高控制系统性能,提出一种双参数模糊调度设计算法,基于时间触发采样的计算机控制系统,对任务的控制性能和空闲时间两个特征参数进行模糊调度设计,构造一个多准则优化调度方法,在正常负载下减少所有任务的截止期错失率,提高系统的可调度性能,在系统超载时保证重要任务稳定的控制性能。
4.针对抖动有界离散控制系统,提出了一种基于补偿控制算法下的稳定性判据,采用区间代数方法构建嵌入式系统实时多任务调度中离散系统闭环区间状态矩阵,利用李雅普诺夫方程和矩阵的无穷范数,导出区间系统的鲁棒稳定充分条件,实例计算表明,该判据具有较小的计算量和保守性,使用简单灵活。
5.针对网络控制系统中长延时(延时超过一个任务周期)问题,提出了一种预测补偿算法与网络调度协同设计方法。当输出信号在传输过程中延时或丢失时,预测补偿算法输出控制预测值到控制对象以维持信号更新,用预测控制补偿算法设计的闭环控制系统的稳定性判据无法保证时,通过网络调度改变任务采样速率,降低控制任务网络负载。仿真表明,该方法在网络延时有界条件下可以提高网络控制系统性能。
6.以深海集矿机控制系统作为应用研究对象,对深海集矿机嵌入式多任务实时控制系统进行协同设计,基于自适应反馈弹性周期任务模型,通过在线自适应调节任务周期,提高集矿机嵌入式实时控制系统的可调度性和优化对象的控制性能。实例仿真说明如何将所提出算法用于解决实时工业控制问题,为实际控制系统协同设计提供指导。
Real-time control systems (RTCS) generally performs multiple parallel control tasks on a real-time operating systems (RTOS), where multiple tasks compete for limited processing resource. The co-design of the real-time control system integrates the control theory and scheduling strategies. In order to achieve the given performance metrics, it is necessary to consider the inherent jitters caused by the scheduling and their influence on the performance when the controller is designed. On the other hand, the controller attributes would be also taken into account in the study of scheduling policy, so that both scheduling flexibility and control performance would be improved. This dissertation studies collaborative method combining efficient scheduling and controller design, aiming at improving the performance of RTCS under the limited processing resource. The main content and innovative work of this dissertation are briefly described as follows:
1. The mutual influence between the control and the scheduling in RTCS was analyzed, so as to provide the baseline support for the collaborative design. The impact from control to scheduling mainly indicates that the different processor load or the different control performance requirement brings different tasks'schedulability in control systems. Otherwise, the sampling jitters and input-output delay jitters caused by scheduling affect the control system performance in various degrees.
2. Focusing on the variation of workload caused by the uncertainty of control tasks in RTCS, the dissertation presents an adaptive feedback elastic period model to deal with the system overload. In this proposed model, CPU utilization is estimated by measuring the execution time of each task, and the system load is dynamically adjusted through self-adaptively modifying the task sample periods online. Thus the schedulability is improved as well as the control performance is guaranteed in RTCS. The simulation results show that this co-design model is effective and useful. It can handle overload situations in a flexible way.
3. To guarantee the schedulability of the critical tasks and enhance the control performance, a FSD (fuzzy scheduling design) algorithm was proposed based on time-triggering sampling computer control systems. The FSD algorithm configures the control index and the idle time of the control tasks with fuzzy design, and deduces a multi-rules optimal scheduling method. In a normal workload, it efficiently decrease the deadline loss ratio and improve the system schedulability. In a overload situation, the performance of the critical task is guaranteed.
4. Focused on the discrete state system whose jitters are not exactly known but within bounded intervals, a stability condition is proposed based on a compensated control algorithm. It represents the discrete system of the embedded real-time multi-tasks scheduling systems as a closed-loop interval matrix by the interval algebra method, and deduces a sufficient robust stability condition using Lyapunov theory and the infinite norm of the matrix. The simulation results show that the method is more computationally effective and less conservative compared to the conventional one.
5. A co-design approach combining predictive control compensation and network scheduling is presented in network-based RTCS to overcome the negative influences of the network long latency, e.g. the latency is longer than a sampling period. When the control signal is lost due to a long time delay or packet losses, the control performance is improved by predicting the latest control value and applying it to the control objective. When the stability criteria under the predictive control compensation algorithm can not be guaranteed, the task period is adjusted through the network scheduling to decrease the network workload. The simulations show that the presented method can improve the performance of the network control systems with the bounded network time-delay.
6. Taking the ocean mining vehicle real-time control system as a practical research example, a co-design method of the ocean mining vehicle real-time control system was presented. Based on the adaptive feedback elastic period model, the schedulability is improved and the plant control performance is optimized with automatically adapting their periods to the situation online. Simulations illustrate how the proposed algorithms apply to real-time industrial problems, which provides guidances for the co-design of the practical systems.
1.分析研究实时控制系统中控制和调度之间的相互影响,为系统协同设计提供支持。控制对调度的影响主要是指不同的处理机负载或不同的控制性能要求都会影响实时系统中任务的可调度性能。反之,调度引起的采样抖动和输入输出时延抖动也会在不同程度上影响控制系统性能。
2.针对控制任务的不确定性引起的实时系统负载变化,提出一种自适应反馈弹性周期模型处理实时系统超载,通过检测任务作业实际执行时间,计算处理机利用率预估值,在线自适应调节任务周期来适应动态变化的系统负载,提高实时控制系统的可调度性,保证控制对象性能。仿真实例表明,该协同设计模型有效实用,并能以更灵活的方式处理实时系统超载。
3.为保证实时控制系统中关键任务的可调度性并提高控制系统性能,提出一种双参数模糊调度设计算法,基于时间触发采样的计算机控制系统,对任务的控制性能和空闲时间两个特征参数进行模糊调度设计,构造一个多准则优化调度方法,在正常负载下减少所有任务的截止期错失率,提高系统的可调度性能,在系统超载时保证重要任务稳定的控制性能。
4.针对抖动有界离散控制系统,提出了一种基于补偿控制算法下的稳定性判据,采用区间代数方法构建嵌入式系统实时多任务调度中离散系统闭环区间状态矩阵,利用李雅普诺夫方程和矩阵的无穷范数,导出区间系统的鲁棒稳定充分条件,实例计算表明,该判据具有较小的计算量和保守性,使用简单灵活。
5.针对网络控制系统中长延时(延时超过一个任务周期)问题,提出了一种预测补偿算法与网络调度协同设计方法。当输出信号在传输过程中延时或丢失时,预测补偿算法输出控制预测值到控制对象以维持信号更新,用预测控制补偿算法设计的闭环控制系统的稳定性判据无法保证时,通过网络调度改变任务采样速率,降低控制任务网络负载。仿真表明,该方法在网络延时有界条件下可以提高网络控制系统性能。
6.以深海集矿机控制系统作为应用研究对象,对深海集矿机嵌入式多任务实时控制系统进行协同设计,基于自适应反馈弹性周期任务模型,通过在线自适应调节任务周期,提高集矿机嵌入式实时控制系统的可调度性和优化对象的控制性能。实例仿真说明如何将所提出算法用于解决实时工业控制问题,为实际控制系统协同设计提供指导。
Real-time control systems (RTCS) generally performs multiple parallel control tasks on a real-time operating systems (RTOS), where multiple tasks compete for limited processing resource. The co-design of the real-time control system integrates the control theory and scheduling strategies. In order to achieve the given performance metrics, it is necessary to consider the inherent jitters caused by the scheduling and their influence on the performance when the controller is designed. On the other hand, the controller attributes would be also taken into account in the study of scheduling policy, so that both scheduling flexibility and control performance would be improved. This dissertation studies collaborative method combining efficient scheduling and controller design, aiming at improving the performance of RTCS under the limited processing resource. The main content and innovative work of this dissertation are briefly described as follows:
1. The mutual influence between the control and the scheduling in RTCS was analyzed, so as to provide the baseline support for the collaborative design. The impact from control to scheduling mainly indicates that the different processor load or the different control performance requirement brings different tasks'schedulability in control systems. Otherwise, the sampling jitters and input-output delay jitters caused by scheduling affect the control system performance in various degrees.
2. Focusing on the variation of workload caused by the uncertainty of control tasks in RTCS, the dissertation presents an adaptive feedback elastic period model to deal with the system overload. In this proposed model, CPU utilization is estimated by measuring the execution time of each task, and the system load is dynamically adjusted through self-adaptively modifying the task sample periods online. Thus the schedulability is improved as well as the control performance is guaranteed in RTCS. The simulation results show that this co-design model is effective and useful. It can handle overload situations in a flexible way.
3. To guarantee the schedulability of the critical tasks and enhance the control performance, a FSD (fuzzy scheduling design) algorithm was proposed based on time-triggering sampling computer control systems. The FSD algorithm configures the control index and the idle time of the control tasks with fuzzy design, and deduces a multi-rules optimal scheduling method. In a normal workload, it efficiently decrease the deadline loss ratio and improve the system schedulability. In a overload situation, the performance of the critical task is guaranteed.
4. Focused on the discrete state system whose jitters are not exactly known but within bounded intervals, a stability condition is proposed based on a compensated control algorithm. It represents the discrete system of the embedded real-time multi-tasks scheduling systems as a closed-loop interval matrix by the interval algebra method, and deduces a sufficient robust stability condition using Lyapunov theory and the infinite norm of the matrix. The simulation results show that the method is more computationally effective and less conservative compared to the conventional one.
5. A co-design approach combining predictive control compensation and network scheduling is presented in network-based RTCS to overcome the negative influences of the network long latency, e.g. the latency is longer than a sampling period. When the control signal is lost due to a long time delay or packet losses, the control performance is improved by predicting the latest control value and applying it to the control objective. When the stability criteria under the predictive control compensation algorithm can not be guaranteed, the task period is adjusted through the network scheduling to decrease the network workload. The simulations show that the presented method can improve the performance of the network control systems with the bounded network time-delay.
6. Taking the ocean mining vehicle real-time control system as a practical research example, a co-design method of the ocean mining vehicle real-time control system was presented. Based on the adaptive feedback elastic period model, the schedulability is improved and the plant control performance is optimized with automatically adapting their periods to the situation online. Simulations illustrate how the proposed algorithms apply to real-time industrial problems, which provides guidances for the co-design of the practical systems.
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