A Fast Iterative Solution Method for Optimizing Engine Fuel Consumption

Abstract

The invention discloses a fast iterative solution method for engine fuel consumption optimization, comprising the following steps: determining the basis of the nonlinear system optimization algorithm, transforming the nonlinear system into a linear system with time-varying disturbance, and determining its optimization objective function and Control law; based on the nonlinear system optimization algorithm, a fast iterative algorithm is proposed: in the first iteration, the disturbance is removed, and the control variable and state variable are obtained by combining the control law; in the second iteration, the control variable and the state variable of the previous iteration are used The state variables update the original nonlinear system, and combine the control law to obtain the control variables and state variables of the second iteration; the nonlinear system is updated in turn, and finally the iterative optimization trajectory is obtained and the law is found; the engine fuel consumption model is established; the proposed fast The iterative algorithm is applied to the established engine fuel consumption model to optimize the engine fuel consumption. The invention can reduce the oil consumption of the engine and reduce the time for optimizing the oil consumption in a relatively simple manner.

Description

technical field [0001] The invention relates to a nonlinear optimization algorithm, in particular to a fast iterative solution method for nonlinear system optimization, which uses the algorithm to optimize engine fuel consumption. Background technique [0002] Optimal control plays an important role in control engineering and is widely used in spacecraft, missiles, satellites and industrial production. Optimal control is to study and solve the problem of finding the optimal control scheme from all possible control schemes. We know that time-varying disturbances are very common in control systems, such as parameter disturbances and dynamic changes, which will seriously affect the control performance. In many cases, since the disturbance is unmeasurable, the disturbance can be estimated first, and then the controller can be designed according to the estimated disturbance. Many contributions have been made in the research and development of disturbance suppressors. [0003] ...

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Problems solved by technology

However, this method cannot obtain the results of nonlinear system optimization in a very short time, and the obtained control law is the sum of infinite terms

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