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The paper proposes a differential flatness theory-based adaptive fuzzy controller for spark-ignited (SI) engines. The system's dynamic model is considered to be completely unknown. By applying a change of variables (diffeomorphism) that is based on differential flatness theory the engine's dynamic model is written in the linear canonical (Brunovsky) form. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. These nonlinear terms are approximated with the use of neuro-fuzzy networks while a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. Moreover, using Lyapunov stability analysis it is shown that the adaptive fuzzy control scheme succeeds H x tracking performance, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H∞ tracking performance. [ABSTRACT FROM AUTHOR]

Highly efficient embedded control units for transportation means make use of advanced nonlinear control and estimation methods. In this research article a new nonlinear filtering and control method is applied to spark ignited (SI) engines. The proposed SI engine's control scheme requires the implementation of differential flatness theory together with a new nonlinear filtering approach (known as Derivative-free nonlinear Kalman Filtering). The considered method succeeds the efficient control of the SI engine parameters such as intake pressure and turn speed. To bring the control loop at a working stage additional problems have to be solved. These are for instance that (i) certain variables of the engine's state vector cannot be measured directly (e.g. the ones associated with input pressure), (ii) there are inaccuracies in the dynamic model of the SI engine while external perturbations and disturbances (such as friction torques) are exerted to the engine. The performance of the proposed control scheme is tested through simulation experiments. [ABSTRACT FROM AUTHOR]

The development of efficient embedded control for turbocharged Diesel engines, requires the programming of elaborated nonlinear control and filtering methods. To this end, in this paper nonlinear control for turbocharged Diesel engines is developed with the use of Differential flatness theory and the Derivative-free nonlinear Kalman Filter. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances the Derivative-free nonlinear Kalman Filter is used and redesigned as a disturbance observer. The filter consists of the Kalman Filter recursion on the linearized equivalent of the Diesel engine model and of an inverse transformation based on differential flatness theory which enables to obtain estimates for the state variables of the initial nonlinear model. Once the disturbances variables are identified it is possible to compensate them by including an additional control term in the feedback loop. The efficiency of the proposed control method is tested through simulation experiments. [ABSTRACT FROM AUTHOR]

The paper studies robust nonlinear control for gas exchange valves in diesel engines, with the use of the Derivativefree nonlinear Kalman Filter. Robust control of gas exchange valves is important for improving the efficiency in the operation of diesel engines. By applying differential flatness theory the initial nonlinear model of the system is transformed in the linear canonical (Brunovsky) form. For the latter model it is possible to design a state feedback controller that enables accurate tracking of the valve's reference set-points. To estimate the nonmeasurable state variables of the model and the unknown external disturbances the Derivative-free nonlinear Kalman Filter is used as a disturbance observer. The Derivative-free nonlinear Kalman Filter consists of the standard Kalman Filter recursion on the linearized equivalent model of the valve and of computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. Evaluation tests are performed for assessing the performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]