Vehicle ISD suspension active control method based on model reference adaptive control

Abstract

The invention discloses a vehicle ISD suspension active control method based on model reference adaptive control. The method comprises the following steps: (1) establishing a suspension controlled model and a reference model; (2) defining a generalized state error e between the controlled model and the ideal reference model; (3) rewriting a coefficient matrix; (4) judging the stability of the adaptive control system; and (5) determining an adaptive law. A controlled ISD suspension system with a simple structure can track the output of the complex ideal ISD suspension model, and the comprehensive performances of the suspension are improved. The method provides a new research thought and direction for the design and application of the ISD suspension of the vehicle, especially for the research of the controllable ISD suspension.

Description

technical field [0001] The invention belongs to the field of vehicle suspension system control, in particular to the control of vehicle ISD (Inerter-Spring-Damper) suspension system using an inerter device. The invention relates to a control method of a vehicle ISD suspension, in particular to a vehicle ISD suspension control method using an electromechanical inerter. Background technique [0002] The concept of an inerter was proposed by Professor Smith of Cambridge University in 2002. As a mass element with two ends, the inerter breaks through the "grounding" limitation of a single-end mass element, fills in the gap between mass and capacitor in the electromechanical similarity theory, and can be effectively applied to the design of vibration isolation systems. Vehicle ISD suspension is a new suspension vibration isolation system composed of "Inerter-Spring-Damper" (Inerter-Spring-Damper). Vehicle ISD suspension breaks the constraint of the traditional two-element parall...

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Problems solved by technology

Predictive control can use the state of the road ahead as a predictive variable to feed forward, take into account the future target value or disturbance of the system, reduce the peak value of system control energy and control energy consumption, but its calculation is large, which is not conducive to the fast time-varying system. online implementation

For the uncertainty of the suspension control system, control methods such as neural network control and fuzzy control can better deal with complex nonlinear environments, but the learning speed of neural network control is slow and difficult, and the decision-making speed of fuzzy control is comparable to real-time Relatively unstable control speed

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