Joint Robot Tracking Control Under Saturation and Actuator Failure
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Solution Overview
Problem
Joint robot systems face challenges in achieving accurate tracking control due to their highly nonlinear nature and susceptibility to external interference and uncertainty, which complicates the dynamic modeling and leads to drive saturation and actuator failures.
Innovation Solution
A neural network adaptive tracking control method is proposed, incorporating a PID controller and updating algorithms based on robust adaptive and neural adaptive control, which adaptively adjusts controller parameters to handle drive saturation, parameter uncertainty, and non-parametric uncertainty, ensuring fault-tolerance and robustness against external interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional PID control is used for joint robot systems, then the control structure remains simple, but the system cannot handle drive saturation, actuator failures, and external interference effectively
Solution Approach 1:
The control algorithm is segmented into three independent modules: a saturation compensation module that handles actuator saturation, a fault estimation module that detects drive failures, and a robust PID controller that maintains stability. This modular segmentation allows each module to address specific problems independently, improving fault tolerance without requiring complete redesign of the control system.
Solution Approach 2:
The control system dynamically adapts to changing conditions by estimating saturation levels and fault parameters in real-time. The controller gains and compensation terms are continuously updated based on current system state, allowing the system to maintain reliability under varying operating conditions while keeping the base control structure relatively simple.
2Measurement precision
If adaptive control algorithms are implemented to handle uncertainty and interference, then tracking precision improves, but computational complexity and control algorithm complexity increase
Solution Approach 1:
The control system incorporates feedback mechanisms that continuously monitor tracking errors and system state. The estimated saturation levels and fault parameters are fed back into the controller to adjust compensation terms and gains, enabling the system to maintain high tracking precision under uncertainty and interference without requiring excessively complex algorithms.
Solution Approach 2:
The controller dynamically changes parameters such as PID gains and compensation terms based on estimated system conditions. By adapting parameters rather than algorithm structure, the system achieves improved tracking precision while avoiding the exponential complexity increase that would result from more sophisticated adaptive algorithms.
3Reliability
If robust control methods are used to handle external interference and uncertainty, then system reliability improves, but the control algorithm becomes more complex and difficult to implement
Solution Approach 1:
The control design uses intermediate variables such as estimated saturation levels and fault parameters that simplify the relationship between complex disturbances and control actions. These intermediaries act as mediators that translate complex uncertainty and interference into manageable compensation terms, improving robustness while maintaining implementation ease.
Solution Approach 2:
The control system performs self-diagnosis and self-compensation by estimating its own saturation levels and fault parameters. This self-service capability allows the system to maintain robustness against interference and uncertainty without requiring external monitoring or complex implementation, as the controller automatically adapts to its own degraded performance.
Data Source
AI summary
The present disclosure discloses a neural network adaptive tracking control method for joint robots, which proposes two schemes: robust adaptive control and neural adaptive control, comprising the following steps: 1) establishing a joint robot system model; 2) establishing a state space expression and an error definition when taking into consideration both the drive failure and actuator saturation of the joint robot system; 3) designing a PID controller and updating algorithms of the joint robot system; and 4) using the designed PID controller and updating algorithms to realize the control of the trajectory motion of the joint robot. The present disclosure may solve the following technical problems at the same time: the drive saturation and coupling effect in the joint system, processing parameter uncertainty and non-parametric uncertainty, execution failure handling during the system operation, compensation for non-vanishing interference, and the like.