Wheel-Legged Robot LQR Control for Inverted Pendulum Stability
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Solution Overview
Problem
Two-wheeled legged robots are susceptible to instability and falls due to disturbances from unknown terrains or external factors, necessitating a robust motion control method to maintain stable body posture.
Innovation Solution
A method involving a two-wheeled inverted pendulum model is constructed to simplify dynamics, followed by linear quadratic regulator (LQR) control for real-time computation of wheel torques, combined with feedback control frameworks using virtual spring-damper elements and hip joint driving torques to stabilize the robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a two-wheeled legged robot uses complex motion control to maintain stability on unknown terrains, then stability improves, but computational complexity and computation time increase
Solution Approach 1:
The control system is divided into multiple independent modules: terrain estimation module, state observer module, and LQR control module. Each module processes specific aspects of robot control separately, reducing overall computational complexity while maintaining stability through coordinated operation of these segmented functional blocks
Solution Approach 2:
The system performs preliminary terrain estimation and state prediction before actual control execution. By pre-calculating terrain characteristics and predicting robot states, the control algorithm prepares optimal control inputs in advance, reducing real-time computational burden while ensuring stable response to terrain disturbances
2Reliability
If real-time control computation is enhanced to respond quickly to disturbances, then stability improves, but computation time may increase
Solution Approach 1:
The system implements continuous feedback through state observers that monitor robot orientation, velocity, and terrain conditions in real-time. This feedback mechanism enables the LQR controller to adjust control inputs dynamically based on current system state, ensuring rapid response to disturbances while maintaining efficient computation through iterative optimization
Solution Approach 2:
The control algorithm dynamically adjusts LQR weighting parameters and observer gain parameters based on operating conditions and terrain characteristics. By adapting these parameters in real-time, the system optimizes computation time for different scenarios while maintaining stability across varying terrain conditions and disturbance levels
Data Source
AI summary
A robot control method includes: building a two-wheeled inverted pendulum model based on a wheel-legged robot; constructing initial state-space equations based on the two-wheeled inverted pendulum model; linearizing the initial state-space equations to obtain the state-space equations for a linear time-invariant system; obtaining a quadratic performance objective function according to the state-space equations for the linear time-invariant system; and solving the quadratic performance objective function by a linear quadratic regulator to obtain wheel torques of the wheel-legged robot, and controlling the wheel-legged robot according to the wheel torques.