目的 基于控制器的运动生成方法存在一些局限性，对骨骼参数、运动风格变化的适应性较差。提出一种面向多骨骼及多风格的行走运动控制器及其生成方法。方法 首先使用改进的比例微分控制器作为预处理，使仿真过程适应较大的比例微分增益，然后应用特定规则调整比例微分系数并使用旋转迭代算法进行优化，最后设置各种与稳定性和风格相关的目标函数，使用协方差矩阵自适应进化策略对控制器中的目标姿态进行优化。结果 本文方法可以生成一系列对应不同骨架、不同运动风格的行走控制器，在效率、稳定性、鲁棒性和多样性方面相较其他方法有一定优势，其中稳定运行时间可提高一个数量级。结论 本文方法生成的控制器运动稳定性好，风格多样可控，不需要大量手工调整，不要求用户具有较强专业背景，增强了控制器的适应性，扩展了控制器生成运动的应用范围。

Objective Motion generation methods based on motion controllers are widely investigated and cause difficulty in computer animation. These methods are limited by poor adaptabilities of bone parameters and style variations. Given these challenges, current motion controllers cannot rapidly adapt to various user demands. This paper proposes a multi-skeleton, multi-style-oriented locomotion controller, and describes the method for its generation. Method First, we use an improved proportional derivative controller for preprocessing, with the aid of heuristic method to adopt the high proportional and derivative gains during our simulation process. We then apply specific rules based on skeletal variations to tune the proportional and derivative parameters of all joints. Next, we apply the twiddle iteration algorithm to optimize the proportional and derivative parameters of hip joint for the purpose of improving motion stability. Finally, we set up several objective functions aimed at maintaining motion stability and expanding motion style diversity to optimize the target pose of the controllers, and we choose the covariance matrix adaption evolution strategy to process the optimization. Result Experimental results show that the proposed method can generate a series of walking controllers mapped to different skeletal parameters and styles. Furthermore, the method is efficient, stable, robust, and diverse in styles. Specifically, the stability of the method is an order of magnitude higher than that of other methods. Conclusion The proposed method can generate motions with good stability and changeable styles in certain degrees. The proportional derivative parameters and target poses are generated automatically through optimization, and only initial configuration sets are required. Our method can easily be learned by users with less professional skills. This study thus improves adaptability of motion generation methods based on motion controllers, and expands the application scope of motion controllers.