Abstract
A hysteresis model was built to describe the backlash of the flow regulator in a solid ducted rocket, and its influence on the engine control was also analyzed in this study. An adaptive backlash compensation method was proposed under two challenges: limited information and backlash state variation caused by the harsh environment in the gas generator. The touch state is designed and its observation is used to get the state of backlash, and a compensation control method using the existing information was carried out combined with the motion intention. This method greatly shortened the time during the transition and reduced the hysteresis effect on the control system. Furthermore, the compensation method is improved and acquires a self-learning ability, the compensation parameter changes adaptively during the process of flow regulation, and it is able to meet the challenge of an unknown and variable state of backlash. Finally, the validation of the compensation method was carried out with two simulations.
Highlights
A solid ducted rocket is a propulsion device developed based on solid rocket and ramjet technology
An adaptive backlash compensation method was proposed under the challenge of limited information caused by the limit of the sensor in an aircraft and the challenge of backlash state variation caused by the harsh environment in the gas generator
The touch state of a backlash is designed, and its observation using existing information is carried out to get the state of the backlash, including its size and existing direction
Summary
A solid ducted rocket is a propulsion device developed based on solid rocket and ramjet technology. In reference [7], the friction generated in gas flow regulation of ducted rockets is considered, a compensation method is discussed to eliminate its effect, and its validation is carried out by a simulation result. Based on the touch state observation, a new backlash compensation method was proposed, and the hysteresis of flow regulation has been solved effectively. On this basis, the compensation method can be improved and a selflearning ability acquired; the compensation parameter will change adaptively, and it can meet the challenge that the state of backlash is unknown or changing during the process of flow regulation. The compensation method was discussed in detail and its validation was carried out with simulation in two cases: constant backlash state and changing backlash state
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