The efficient and emission reducing control of the air path of an internal combustion engine is a challenging task due to its nonlinear and multivariate nature. By applying the well-known local model network approach to describe the nonlinear process in terms of linear operating point approximations, a fast and efficient model generation through data-driven system identification can be achieved. In this paper it is demonstrated how a nonlinear multivariate model predictive controller can be synthesised from the identified model directly by exploiting its representation in flatness coordinates. For the proposed controller, a compactly formulated quadratic programme results. Because of the uniform representation of all local models in controllability canonical form, a state observer is rendered unnecessary. Additionally, input and output constraints can be taken into account in the optimisation directly. The effectiveness of the control scheme is demonstrated successfully in jointly controlling the exhaust manifold pressure and the engine out NO${}_{\mathrm{x}}$ concentration for a heavy-duty engine on the testbed.

由于其非线性和多元性质，对内燃机的空气路径进行有效的减排控制是一项艰巨的任务。通过使用众所周知的局部模型网络方法以线性工作点逼近来描述非线性过程，可以通过数据驱动的系统识别实现快速有效的模型生成。本文证明了如何通过利用平面度坐标中的表示直接从识别的模型中合成非线性多元模型预测控制器。对于所提出的控制器，可以紧凑地制定二次程序。由于所有局部模型均以可控规范形式统一表示，因此不需要状态观察器。此外，在优化过程中可以直接考虑输入和输出约束。通过联合控制排气歧管压力和发动机熄火，成功证明了该控制方案的有效性。${}_{\mathrm{X}}$ 试验台上重型发动机的浓度。