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New environmental legislation on emission and fuel efficiency targets increasingly requires good transient engine performance and this in turn means that the previously acceptable static engine calibration and control methodologies based on steady-state testing must be re-placed by dynamical optimization using dynamical models. Although many advances have been made in predictive models for internal combustion engines, the phenomena involved are so many, complex and nonlinear that dynamical black-box models typically employing neural network structures must be determined from system identification through experimental testing. Such identified dynamical models are required to provide high accuracy multiple step-ahead predictions of emissions but must accordingly also be compactly implementable for speed and memory to allow for the required large scale optimization involving possibly many thousands of iterations.

This paper presents an experimental calibration method for the feedforward fuelling controller for a PFI SI engine. A recently proposed method [1] is extended from the idle to the torque delivery region and uses a Riccati designed rather than Parameter-Space linear element. Dynamic input signals are applied to air path, load and fuel entering the engine to excite the air-to-fuel ratio dynamics. A nonlinear inverse compensator is obtained directly from the observed input-output behaviour. Least squares black-box identification is used to generate the compensator using an algebraic NARX structure. The resulting inverse compensator not only acts as the feedforward controller but also linearises the fuelling path and therefore makes the system well suited for robust linear feedback control. The feedforward compensator is experimentally demonstrated and subsequently a robust H-infinity feedback controller is designed, implemented and the complete system experimentally validated.

An identification based approach to the design and implementation of a Kalman-filter feedback control method for the transient chassis dynamometer is presented. The requirements for torque controllers for high speed transient chassis dynamometers for road-load simulation are discussed. A common significant problem with feedback control in the conventional transient chassis dynamometer is due to resonances arising from the structural dynamics especially in the load centre linkage. A Kalman filter based filter and control method is proposed to address this issue and to provide significantly enhanced performance over that achieved by current controllers. Particular attention is paid to obtaining relative stability margins in all filter and control loops for robust torque feedback control by the use of H-infinity methods. The filter and controller designs are based on easily implementable identification methods.