新能源汽车用高强度刹车片成型动态浮力称量控制方法研究与应用

With the rapid development of new energy vehicles, the demand for brake pads, the most important component of the brake system,has been increasing day by day, and the problems of easy wear and poor durability have become increasingly prominent. The development of online weighing equipment is extremely urgent. Brake pad forming online weighing and batching processing is essentially a complex,un-certain, multi parameter and strong coupling system. And the existing resistance strain type weighing sensing mechanism and control method can’t realize the brake pads with high efficient and high quality molding, and it urgently needs new technology and new theore-tical methods to be a breakthrough. This project will be establish online ingredients buoyancy weighing nonlinear model from the inquiry, the high performance brake piece molding line ingredient buoyancy weighing process parameters, and further by orthogonal test to obtain the complete data and grey relational analysis, theory and relevance vector machine method based on data driven. This project will be study on the weak buoyancy weighing signal from complex external stochastic nonlinear, non Gaussian interference in real time extraction based on active noise control and characteristic signal separation combined, and Adaptive Robust Control Based on Neural Network to achieve brake pad forming of online weighing and batching accurate and efficient control. This project belongs to the control, and microelectronics process-ing materials and interdisciplinary frontier research, is expected to break through the bottleneck of brake pads, high quality forming online weighing and batching equipment, and broaden the application field of nonlinear control theory.

伴随新能源汽车的高速发展，其制动系统重要零部件刹车片的需求与日俱增，刹车片易损耗、耐用性差的问题日益凸显，研发新能源汽车刹车片成型动态配料称量装备极为迫切。刹车片成型动态配料称量过程实质是一类复杂的时滞、不确定、多参数强耦合系统，现有电阻应变式称重传感机理难以实现刹车片高效高质成型，亟需新技术和新理论方法予以突破。本项目将从探究高效高性能刹车片成型在线配料浮力称量过程工艺参数入手，由正交试验获取完备数据并进行灰关联分析；基于数据驱动理论和相关向量机方法，建立在线配料浮力称量非线性模型；基于主动噪声控制与特征信号分离相结合，研究浮力称重信号从复杂的外部非线性、非高斯干扰中的实时提取和基于神经网络的自适应鲁棒控制，实现刹车片成型动态称量精准高效控制。本项目隶属于控制、微电子和材料加工等多学科交叉的前沿研究，有望突破刹车片高效高质成型在线配料称量装备的关键瓶颈，拓宽非线性控制理论的应用领域。

伴随新能源汽车的高速发展，其制动系统重要零部件刹车片的需求与日俱增，刹车片易损耗、耐用性差的问题日益凸显，研发新能源汽车刹车片成型动态配料称量装备极为迫切。刹车片成型动态配料称量过程实质是一类复杂的时滞、不确定、多参数强耦合系统，现有电阻应变式称重传感机理难以实现刹车片高效高质成型，亟需新技术和新理论方法予以突破。本项目的研究围绕以下四方面展开：基于正交试验的在线配料浮力称量过程灰色关联分析；建立新能源汽车刹车片在线配料浮力称量非线性模型；提出刹车片成型在线配料浮力称量系统的自适应控制器设计理论与方法；开展汽车刹车片成型装备综合性能验证。基于上述研究内容，本项目取得以下进展：建立了新能源汽车刹车片在线配料浮力称量非线性模型，并优化了传递链函数。针对刹车片成型在线配料具有输入饱和的未知严格反馈非线性系统，提出了一种新的基于辅助系统的设计策略，构造了一种基于障碍函数的自适应跟踪控制器，提高了系统抗干扰性、可靠性及在线配料浮力称量准确度等参数。基于本项目，发表学术期刊论文18篇（SCI收录论文17篇，EI收录论文1篇）、申请并获授权发明专利2件。获山东省机械工业科学技术奖一等奖1项、二等奖1项、三等奖1项。在人才培养方面，依托项目，入选泰山学者特聘专家计划1人、培养了博士研究生1人，硕士研究生2人。