采用双闭环PID控制及PSO算法的双足轮式倒立摆控制系统

为了解决双足轮式倒立摆的稳定控制问题,提出一种采用双闭环PID的控制器及粒子群优化算法(Particle swarm optimization, PSO)的双足轮式倒立摆控制系统,并设计与控制效果相关的代价函数,以增加控制系统的稳定性,在matlab仿真条件下,实现了双足轮式倒立摆的稳定控制,实验结果表明,经过整定的控制器参数较原参数的阶跃响应更好,上升时间和稳定时间较未整定PID控制器的短,并且本算法已经应用到基于面阵CCD的直立式智能搜索识别采样系统中,验证了PSO-PID控制器的可用性。

Information manifold and fault detection of multi-agent systems

With the increase of the number of agents in multi-agent systems and the rapid increase of the complexity of the overall structure of the system,the fault detection and diagnosis work has brought great challenges.Researchers have carried out considerable research work on fault detection and diagnosis of multi-agent systems,but there is no research on fault state estimation and diagnosis based on the information and state of the whole multi-agent system.Based on the global perspective of information geometry theory,this paper presents two new physical quantities of the information manifold of multi-agent systems,as Lagrangian and energy–momentum tensor,to express the state of the overall information of multi-agent systems,and to characterize the energy state and development trend of faults.In this paper,two new physical parameters are introduced into the research of multi-agent fault detection and diagnosis,and the fault state and trend of multi-agent system are evaluated from the global perspective,which provides more comprehensive theoretical support for designing more scientific and reasonable fault diagnosis and fault recovery strategies.Simulation of the application example confirms the competitive performance of the proposed method.