This paper focuses on optimizing an unknown cost function through extremum seeking(ES)control in the presence of a slow nonlinear dynamic sensor responsible for measuring the cost.In contrast to traditional perturbati...This paper focuses on optimizing an unknown cost function through extremum seeking(ES)control in the presence of a slow nonlinear dynamic sensor responsible for measuring the cost.In contrast to traditional perturbation-based ES control,which often suffers from sluggish convergence,the proposed method eliminates the time-scale separation between sensor dynamics and ES control by using the relative degree of the nonlinear sensor system.To improve the convergence rate,the authors incorporate high-frequency dither signals and a differentiator.To enhance the robustness with the existence of rapid disturbances,an off-the-shelf linear high-gain differentiator is applied.The first result demonstrates that,for any desired convergence rate,with properly tuned parameters for the proposed ES algorithm,the input of the cost function can converge to an arbitrarily small neighborhood of the optimal solution,starting from any initial condition within any given compact set.Furthermore,the second result shows the robustness of the proposed ES control in the presence of sufficiently fast,zero-mean periodic disturbances.Simulation results substantiate these theoretical findings.展开更多
目的分析达芬奇机器人辅助直肠及乙状结肠癌根治术的学习曲线,探索最佳的学习例数.方法回顾性分析2021年7月至2022年12月于中国医学科学院肿瘤医院由同一术者完成的93例达芬奇机器人辅助直肠及乙状结肠癌根治术的临床资料.根据手术时间...目的分析达芬奇机器人辅助直肠及乙状结肠癌根治术的学习曲线,探索最佳的学习例数.方法回顾性分析2021年7月至2022年12月于中国医学科学院肿瘤医院由同一术者完成的93例达芬奇机器人辅助直肠及乙状结肠癌根治术的临床资料.根据手术时间构建达芬奇机器人辅助直肠及乙状结肠癌根治术的学习曲线,根据曲线峰值确定最佳学习例数,并按照学习曲线将患者分为手术提高组(A组)及手术熟练组(B组),对比两组临床指标.结果93例患者顺利实施达芬奇机器人辅助乙状结肠或直肠癌根治术,最佳拟合曲线方程为:根据CUSUM值进行曲线拟合,曲线方程为y=0.006x3-1.183x2+58.840x-219.293,R2=0.857,P<0.01.达到学习曲线最佳手术例数为33例.将前33例患者列为A组,将之后的60例患者列为B组.与A组相比,B组的手术所需时间更短(144.3 min vs.179.1 min,P=0.008),失血量更少(21.7 mL vs.29.4 mL,P=0.010),术后住院时间更短(7.1 d vs.8.8 d,P=0.026)o结论达芬奇机器人手术需要一定的学习例数.本研究表明33例为最佳学习例数.完成33例达芬奇机器人手术学习后,患者手术时间更短,失血量更少,术后住院时间更短.展开更多
基金supported by the Australian Research Council Discovery under Grant No.DP200102402.
文摘This paper focuses on optimizing an unknown cost function through extremum seeking(ES)control in the presence of a slow nonlinear dynamic sensor responsible for measuring the cost.In contrast to traditional perturbation-based ES control,which often suffers from sluggish convergence,the proposed method eliminates the time-scale separation between sensor dynamics and ES control by using the relative degree of the nonlinear sensor system.To improve the convergence rate,the authors incorporate high-frequency dither signals and a differentiator.To enhance the robustness with the existence of rapid disturbances,an off-the-shelf linear high-gain differentiator is applied.The first result demonstrates that,for any desired convergence rate,with properly tuned parameters for the proposed ES algorithm,the input of the cost function can converge to an arbitrarily small neighborhood of the optimal solution,starting from any initial condition within any given compact set.Furthermore,the second result shows the robustness of the proposed ES control in the presence of sufficiently fast,zero-mean periodic disturbances.Simulation results substantiate these theoretical findings.
文摘目的分析达芬奇机器人辅助直肠及乙状结肠癌根治术的学习曲线,探索最佳的学习例数.方法回顾性分析2021年7月至2022年12月于中国医学科学院肿瘤医院由同一术者完成的93例达芬奇机器人辅助直肠及乙状结肠癌根治术的临床资料.根据手术时间构建达芬奇机器人辅助直肠及乙状结肠癌根治术的学习曲线,根据曲线峰值确定最佳学习例数,并按照学习曲线将患者分为手术提高组(A组)及手术熟练组(B组),对比两组临床指标.结果93例患者顺利实施达芬奇机器人辅助乙状结肠或直肠癌根治术,最佳拟合曲线方程为:根据CUSUM值进行曲线拟合,曲线方程为y=0.006x3-1.183x2+58.840x-219.293,R2=0.857,P<0.01.达到学习曲线最佳手术例数为33例.将前33例患者列为A组,将之后的60例患者列为B组.与A组相比,B组的手术所需时间更短(144.3 min vs.179.1 min,P=0.008),失血量更少(21.7 mL vs.29.4 mL,P=0.010),术后住院时间更短(7.1 d vs.8.8 d,P=0.026)o结论达芬奇机器人手术需要一定的学习例数.本研究表明33例为最佳学习例数.完成33例达芬奇机器人手术学习后,患者手术时间更短,失血量更少,术后住院时间更短.
