In Sports, specifically, badminton is a dynamic and skill motions for participants. Previous robots had difficulties behaving as human beings because of their high limitations of low operational speed, heavy bodies, and basic mechanisms. This paper introduces two control approaches to improve a robot's energy effectiveness which needs to make point-to-point motions over a set period. The first method is based on an Adaptive Proximate Energy-Optimized Servo Algorithm (APEOSA) which has optimized parameters for energy efficiency. The second strategy is a Model Predictive Control Strategy (MPCS) to energy management. The technique has been created for a Badminton robot. The robot may still intercept several competitor transportation units on time and in both cases, there is a significant decrease in energy consumption which has been minimized during experimental analysis. An enormous energy-saving, about 40%, is accomplished using EOMPCS and APEOSA compared to APTOS, with the same positioning error as faced by EOMPCS and APTOS.

特别是在体育运动中，羽毛球是参与者的动态运动和技能运动。先前的机器人由于操作速度低，重量重和基本机制的局限性而难以像人类一样表现。本文介绍了两种控制方法来提高机器人的能效，这需要在设定的时间内进行点对点运动。第一种方法基于自适应近距离能量优化伺服算法（APEOSA），该算法具有优化的能效参数。第二种策略是能源管理的模型预测控制策略（MPCS）。该技术是为羽毛球机器人创建的。机器人仍然可以按时拦截几个竞争对手的运输单位，在两种情况下，能耗的显着降低，在实验分析过程中已将其降至最低。与APTOS相比，使用EOMPCS和APEOSA可以实现大约40％的巨大节能，并且定位误差与EOMPCS和APTOS相同。