Introduction: The proportion of tactical activities athletes perform through explosive strength is relatively high in the authentic game of badminton. And weight training has the effect of improving the explosive power of players. Objective: Study the effects of weight training incorporated with explosive strength training on badminton players. Methods: The article adopts a controlled experiment, in which the control group practices standardized explosive strength training in training activities. In contrast, the experimental group has the addition of weightlifting exercises using sandbags in its protocol. The experiment was performed completely according to the dedicated badminton teaching plan for freshmen, lasting eight weeks. Results: Before the intervention, the wrist joint speed in the experimental group was 11.76 km/h, and the final speed was 162.30 km/h. After the experiment, the joint velocity increased to 12.35 km/h, and the final velocity to 177.50 km/h. Conclusion: The addition of 10% weight training showed statistical benefits to explosive strength training, and its implementation in usual protocols is indicated to improve the indicators of explosive strength in athletes. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
The numerical solution of the sports intelligent learning system has high complexity during operation, which restricts the online application of optimal control. In order to improve the optimization effect of the feedback delay of the sports intelligent learning system, this paper discusses the method of efficiently solving the optimal control problem of the differential-algebraic system from the aspects of improving the efficiency of the integration process and selecting an appropriate constraint processing strategy. Moreover, this paper proposes an efficient calculation method for solving the index-1 DAE optimal control problem under continuous inequality constraints. This method avoids a large number of interior point constraints introduced by discretization of continuous inequality constraints and makes it possible to solve optimal control problems under continuous inequality constraints. In addition, this paper designs a one-step advanced model predictive control algorithm to solve the NLP problem one sampling period in advance, and then correct the solution of the NLP problem through sensitivity analysis. Finally, this paper designs experiments to study the performance of the method proposed in this paper. The research results show that the method constructed in this paper is effective.
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