Autonomous Control System Disturbance Compensation via State Space Integration
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Solution Overview
Problem
Existing control systems for autonomous and semi-autonomous systems face challenges in accurately compensating for external disturbances, such as wind or uneven terrain, which can cause deviations from planned trajectories, leading to inaccurate tracking and delayed reaction times.
Innovation Solution
A control system that detects disturbances by comparing observed and estimated states, incorporating disturbance data directly into the control command generation process, allowing for real-time compensation without additional processing steps, using a disturbance observer to identify external forces and integrate them into the state space formulation for Model Predictive Control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing error compensation measures track error state over time and apply compensation after control output generation, then steady state errors are compensated, but the system introduces additional processing delay and reduces real-time performance
Solution Approach 1:
The disturbance observer estimates external disturbances in advance and feeds this information to the MPC controller before control commands are generated. This preliminary disturbance estimation allows the controller to proactively compensate for anticipated disturbances rather than reacting to errors after they occur, eliminating the need for separate post-processing compensation steps and reducing overall system delay
Solution Approach 2:
The system implements a feedback loop where the disturbance observer continuously monitors system state deviations caused by external disturbances and feeds this disturbance information back to the MPC controller. This real-time feedback enables the controller to dynamically adjust control commands to counteract disturbances, achieving accurate compensation without introducing additional processing delays
2Measurement precision
If disturbance tracking is performed separately from state space used for control outputs, then error accumulation can be compensated, but the compensation process becomes error-prone when disturbances are not in steady state
Solution Approach 1:
The disturbance observer and state space model are merged into a unified framework where disturbance estimation is performed within the same state space used for control optimization. The disturbance states are integrated directly into the MPC formulation, allowing simultaneous estimation and control optimization. This unified approach ensures that disturbance compensation remains reliable even when disturbances are transient or non-steady, as the system adapts to changing disturbance conditions through real-time state estimation
Data Source
AI summary
The present disclosure relates to determining an observed state of a system based at least on sensor data generated using one or more sensors of the system. The present disclosure further relates to generating disturbance data based at least on comparing an estimated state of the system with the observed state of the system. The present disclosure further relates to updating one or more disturbance terms of a state space formulation based at least on the disturbance data. The present disclosure further relates to generating, based at least on the state space formulation, a control command that directs one or more operations of the system according to plan data indicative of a plan for completing one or more tasks of the system.