计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
18期
62-68
,共7页
六维力传感器%下E型膜%野草算法%小生境技术%扩展卡尔曼滤波器
六維力傳感器%下E型膜%野草算法%小生境技術%擴展卡爾曼濾波器
륙유력전감기%하E형막%야초산법%소생경기술%확전잡이만려파기
six-axis force sensor%lower E-type membrane%invasive weed optimization%niche technology%extended Kal-man filtering
为减小噪声信号对六维力传感器测量精度的影响，同时解决因主振型信息缺失导致扩展Kalman滤波器难以获得最优系统估计的问题，提出一种基于小生境野草算法优化的扩展卡尔曼滤波（NIWO-EKF）算法。算法根据正弦激励力响应与应变之间的关系，构建六维力传感器下E型膜非线性系统模型。将系统干扰矩阵与控制矩阵视为一个整体，引入野草繁殖思想，以前6阶主振型信息构成的综合矩阵为均值，进行高斯采样，产生初始化的可行解。将小生境技术与野草算法相融合，利用野草算法进行全局搜索，根据适应度的大小对个体进行降序排列，按照小生境容量划分出多个种群协同合作，避免搜索过程陷入局部最优，提高算法的寻优精度和收敛速度。采用改进野草算法对EKF中的系统干扰控制矩阵进行优化处理。仿真实例表明，优化后的扩展卡尔曼滤波器能有效地提高六维力传感器的测量精度，具有很好的鲁棒性和稳定性。
為減小譟聲信號對六維力傳感器測量精度的影響，同時解決因主振型信息缺失導緻擴展Kalman濾波器難以穫得最優繫統估計的問題，提齣一種基于小生境野草算法優化的擴展卡爾曼濾波（NIWO-EKF）算法。算法根據正絃激勵力響應與應變之間的關繫，構建六維力傳感器下E型膜非線性繫統模型。將繫統榦擾矩陣與控製矩陣視為一箇整體，引入野草繁殖思想，以前6階主振型信息構成的綜閤矩陣為均值，進行高斯採樣，產生初始化的可行解。將小生境技術與野草算法相融閤，利用野草算法進行全跼搜索，根據適應度的大小對箇體進行降序排列，按照小生境容量劃分齣多箇種群協同閤作，避免搜索過程陷入跼部最優，提高算法的尋優精度和收斂速度。採用改進野草算法對EKF中的繫統榦擾控製矩陣進行優化處理。倣真實例錶明，優化後的擴展卡爾曼濾波器能有效地提高六維力傳感器的測量精度，具有很好的魯棒性和穩定性。
위감소조성신호대륙유력전감기측량정도적영향，동시해결인주진형신식결실도치확전Kalman려파기난이획득최우계통고계적문제，제출일충기우소생경야초산법우화적확전잡이만려파（NIWO-EKF）산법。산법근거정현격려력향응여응변지간적관계，구건륙유력전감기하E형막비선성계통모형。장계통간우구진여공제구진시위일개정체，인입야초번식사상，이전6계주진형신식구성적종합구진위균치，진행고사채양，산생초시화적가행해。장소생경기술여야초산법상융합，이용야초산법진행전국수색，근거괄응도적대소대개체진행강서배렬，안조소생경용량화분출다개충군협동합작，피면수색과정함입국부최우，제고산법적심우정도화수렴속도。채용개진야초산법대EKF중적계통간우공제구진진행우화처리。방진실례표명，우화후적확전잡이만려파기능유효지제고륙유력전감기적측량정도，구유흔호적로봉성화은정성。
To reduce the influence of the noise for the measurement accuracy of the six-axis force sensor and solve the problem that the Extended Kalman filtering can’t gain the optimal system noise matrix, a new Extended Kalman Filtering (EKF)based on Niche Invasive Weed Optimization(NIWO)has been proposed. The nonlinear state-space model based on the relationship between the response of sinusoidal excitation force and the strain has been established. The idea of the grass breeding has been introduced to achieve the Gauss sampling of system interference matrix consisted of first six-order vibration mode information and to produce the initial feasible solutions. After combining niche technology with Invasive Weed Optimization(IWO), the global search of the new algorithm has been executed by the IWO. According to fitness value, the individual has been arranged in descending order. Multiple populations can be carved out to collaborate on the basis of the capacity of the niche. The search processing can be avoided to fall into local optimum. The improved invasive weed optimization algorithm is introduced to optimize the system’s noise matrix in EKF. The simulation results indicate that the new algorithm has better robustness and real-time performance. It can effectively enhance the measurement accu-racy of six-axis force sensor.
