资源受限嵌入式机电控制系统功耗感知调度

针对嵌入式机电控制系统的能耗受限问题,提出了一种基于功耗感知和反馈调度的能耗管理方法.研究了嵌入式机电控制系统的静态功耗调度模型,提出了一种基于控制代价和系统能耗的联合优化指标,并给出了其优化解.针对在动态调度模型下运算开销大,难以在线实现的问题,采用智能计算方法逼近优化结果.对比了4种智能计算方法的逼近精度和调度开销,采用小波神经网络逼近优化结果.仿真的结果表明:该方法在保证了控制性能的同时,降低了调度开销和系统能耗. 

Power aware scheduling of resource-constraint embedded mechatronic control systems

To solve energy constraint of embedded mechatronic control systems(EMCS), an energy management method based on feedback scheduling and power aware was proposed. The static optimization model of EMCS was discussed. A joint optimization target based on the control cost and the system energy consumption was proposed, and the optimal solution was given. In dynamic scheduling, the overhead of static optimization is very high and difficult to realize, so intelligent computing methods were used to approximate the optimal solution. Scheduling overhead and approximation accuracy of four intelligent computing methods were compared, and wavelet neural network was used to approach optimal results. Results of simulations show that the energy consumption on the base of maintaining control performance was reduced effectively by the proposed method.