Axial Piston Pump Trajectory Planning Under System Limitations
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Solution Overview
Problem
Existing methods for real-time control of hydraulic systems, such as model predictive regulation (MPC) and flatness-based pilot control, face challenges in systematically adhering to physical and geometric limitations, leading to suboptimal or unfeasible trajectories.
Innovation Solution
A method for generating a setpoint trajectory that accounts for control variable and state variable limitations using an advanced state variable filter, which calculates dynamic limitations for the highest derivative of the setpoint trajectory and applies them to ensure compliance with system constraints, allowing for real-time trajectory planning without numerical optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model predictive regulation (MPC) is used to systematically take into account limitations, then control optimality with respect to a defined quality function is improved, but computational complexity and hardware cost increase due to numerical optimization in each iteration step
Solution Approach 1:
The control problem is segmented into two parts: an MPC controller that handles constraint satisfaction and a flatness-based pilot controller that handles optimal trajectory generation. This segmentation allows each controller to specialize in one aspect, reducing the overall computational burden while maintaining systematic adherence to limitations.
Solution Approach 2:
The flatness-based pilot controller pre-calculates optimal trajectories and their derivatives in advance, using the reference value and system model. This preliminary action provides the MPC controller with ready-to-use trajectory information, eliminating the need for real-time numerical optimization and reducing computational complexity during runtime.
2Productivity
If flatness-based pilot control with state variable filter is used for real-time control, then computational effort is reduced, but physical and geometric limitations cannot be systematically taken into account leading to unfeasible or suboptimal trajectories
Solution Approach 1:
The MPC controller uses feedback from the system state and the planned trajectory to adjust the control variables in real-time, ensuring that physical and geometric limitations are systematically taken into account while maintaining real-time control capability.
Solution Approach 2:
The flatness-based pilot controller acts as an intermediary between the reference trajectory and the MPC controller, providing pre-calculated trajectory information that the MPC controller then refines and adjusts to satisfy constraints, combining the advantages of both approaches.
3Speed
If a low-pass filter algorithm (state variable filter) is used for trajectory generation, then real-time control is enabled, but physical and geometric limitations are not considered resulting in unfeasible trajectories
Solution Approach 1:
The system uses dynamic trajectory adjustment where the flatness-based pilot controller continuously adapts the reference trajectory based on current system state and constraints, ensuring both real-time responsiveness and trajectory feasibility through dynamic modification of control parameters.
Solution Approach 2:
The system changes parameters dynamically by adjusting the reference trajectory and its derivatives based on system state and constraints. The flatness-based pilot controller modifies trajectory parameters in real-time to ensure feasibility while maintaining response speed.
Data Source
AI summary
A method is for producing, for a hydraulic machine having an actuator, a setpoint-value trajectory satisfying predefined limitations in order to influence an output variable of the hydraulic machine. A trajectory of unlimited setpoint values is fed to a trajectory planning function, which produces the setpoint-value trajectory from the trajectory of unlimited setpoint values. In the trajectory planning function, the trajectory of unlimited setpoint values is differentiated at least twice in order to obtain a trajectory of unlimited setpoint values that is differentiated n times. In the trajectory planning function, at least one limitation is applied to the differentiated trajectory of unlimited setpoint values in order to obtain a differentiated trajectory of limited setpoint values. The differentiated trajectory of limited setpoint values is fed to a filter integrator chain in order to obtain the setpoint-value trajectory.