Method for PID (proportion integration differentiation) optimization control with dead zone for two-wheeled self-balancing intelligent vehicle

Abstract

A method for PID (proportion integration differentiation) optimization control with a dead zone for a two-wheeled self-balancing intelligent vehicle comprises the following steps that (1) a gyroscope sensor tests and outputs a vehicle body swinging angular velocity signal in real time, an accelerometer sensor tests and outputs a vehicle body angle signal in real time, low-pass filtering and complementary filtering are performed on the vehicle body swinging angular velocity signal and the vehicle body angle signal, and the vehicle body swinging angular velocity signal and the vehicle body angle signal are fused into an accurate pose signal capable of reflecting pose information of a vehicle body in real time; (2) an angular displacement sensor tests and outputs a steering signal in real time; (3) a core control chip acquires the pose signal and the steering signal, performs analog-to-digital conversion on the pose signal and the steering signal, and then performs median average filtering on the pose signal and the steering signal; and (4) the core control chip computes processed data to obtain required control voltage by using PID optimization control with the 'dead zone', the control voltage is outputted to a left motor and a right motor in a PWM (pulse-width modulation) mode, and the two-wheeled self-balancing intelligent vehicle is stably controlled. By using the method, the response speed is high, interference is effectively avoided, and control stability and comfort of the two-wheeled self-balancing intelligent vehicle are guaranteed.

Description

technical field [0001] The invention relates to a control method of a vehicle, in particular to a PID optimization control method with a "dead zone" of a two-wheel self-balancing intelligent vehicle, and belongs to the technical field of automatic control. Background technique [0002] The world today is going through a technological revolution, and so is the field of simple and practical mobility scooters. A novel, simple, practical, and highly reliable means of transportation will bring a lot of convenience to people. Two-wheel self-balancing intelligent vehicles, also known as two-wheel self-balancing intelligent robots, are just the rookies of this type of means of transportation. [0003] In modern control theory and application, there is a commonly used control principle of inverted pendulum. The inverted pendulum itself is a complex, nonlinear and unstable system, and the two-wheeled self-balancing smart car itself also has the characteristics of nonlinearity, strong...

AI Technical Summary

Problems solved by technology

At present, some common two-wheeled self-balancing vehicles have obvious speed fluctuations during operation, unstable control, and the effective control angle is generally small (usually between ±0-6°)

In addition, the position and attitude sensors commonly used in current balancing cars are gyroscopes and accelerometers, which are very sensitive to high-frequency noise interference, and the control process does not allow obvious lag, which puts a high demand on the control system of the car. The control of the system poses great challenges

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