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As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included.

A non-linear generalised minimum variance (NGMV) control law is derived for systems represented by an input–output state dependent non-linear (NL) subsystem that may be open-loop unstable. The solution is obtained using a model for the multivariable discrete-time process that includes a state-dependent (NL and possibly unstable) model that links the output and any ‘unstructured’ NL input subsystem. The input subsystem can involve an operator of a very general NL form, but this has to be assumed to be stable. This is the first NGMV control solution that is suitable for systems containing an unstable NL sub-system which is contained in the state-dependent model. The process is also assumed to include explicit common delays in input or output channels. The generalised minimum variance cost index to be minimised involves both error and control Q1 signal costing terms but to increase generality weighted states are also included in the cost index. The controller derived is simple to implement considering the complexity of the system represented. If the plant is stable the controller structure can be manipulated into an internal model control form. This form of the controller is like an NL version of the Smith Predictor which is valuable for providing confidence in the solution

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A Nonlinear Predictive Generuli::ed Minimum Variance (NPG!vfV) control algorithm is introduced for the control of nonlinear discrete-time multivariable Ａ Ｇ ｊ ｾ ｹ ｳ ｴ ･ ｭ ｳ The plant model is repre ented by the combination of a very gen ral nonlinear op rator and also a linear subsystem which can be open-loop unstable and is represented in state-pac mod I form. The multi-step predictive control cost index to be minimised involve both weighted error and control signal costing terms. The solution for the control law is derived in the time-domain using a general op rator representation of the process. he controller include, an int rnal m del of the nonlinear proce s but because of the assumed structure of the system the state observ I' is only required to be linear. In the asymptotic case, where the plant is linear, the controll I' reduces to a state-space v rsion of the well known GPC controller.