Two-Wheeled Robot Balance Control Under State Uncertainty
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Solution Overview
Problem
Two-wheeled robots face challenges in maintaining balance due to changes in operating conditions and state estimation errors, which affect the optimality of balance control and stability.
Innovation Solution
A method that calculates optimal feedback gains, variable matrices, and uncertainty based on state variables to adjust control torque and maintain balance, incorporating adaptive adjustments to compensate for deviations and uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a balance controller is designed with fixed feedback gains to maintain balance, then the initial balance control can be achieved, but the control optimality deteriorates when operating conditions change
Solution Approach 1:
The patent implements dynamic adjustment of feedback gains and control parameters based on real-time state variables. The controller continuously calculates optimal feedback gains using state variables (position, velocity, acceleration) and updates control parameters adaptively, transforming the fixed-gain controller into a dynamic one that maintains optimality under varying operating conditions.
Solution Approach 2:
The patent changes control parameters (feedback gains, control torque) based on state variables and operating conditions. By calculating optimal feedback gains from state variables and adjusting control parameters dynamically, the system adapts to different operating conditions while maintaining balance control optimality.
2Reliability
If state estimation is performed to maintain balance, then balance control can be achieved, but estimation errors affect control precision
Solution Approach 1:
The patent uses feedback of state variables (position, velocity, acceleration) to continuously update control decisions. By incorporating real-time state feedback and calculating optimal feedback gains from these variables, the system compensates for estimation errors and maintains accurate balance control despite initial estimation uncertainties.
Solution Approach 2:
The patent performs preliminary calculation of optimal feedback gains and control parameters based on current state variables before executing balance control. This advance preparation of control parameters based on estimated state reduces the impact of estimation errors by pre-compensating for expected deviations.
3Device complexity
If fixed control parameters are used to ensure simple control implementation, then device complexity is reduced, but control precision deteriorates under varying conditions
Solution Approach 1:
The controller performs self-adjustment by automatically calculating optimal feedback gains and control parameters based on its own state variables. The system serves itself by using its measured state (position, velocity, acceleration) to determine the appropriate control action, eliminating the need for external parameter tuning while maintaining high precision.
Solution Approach 2:
The patent dynamically changes control parameters (feedback gains, control torque) based on state variables rather than using fixed values. This parameter adaptation maintains control precision under varying conditions while the automated calculation process keeps implementation complexity manageable.
Data Source
AI summary
A robot control method includes calculating an optimal feedback gain, an optimal variable matrix, and uncertainty according to a first state variable and a first feedback gain of a robot. The optimal variable matrix represents a degree of a gain that a motion state of the robot has on a control mode of the robot. The method further includes calculating an angle deviation matrix and a noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and a second state variable of the robot, obtaining a control torque of the robot according to the second state variable, the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix, and controlling the robot according to the control torque.