Motion Simulator Torque Compensation for Joint-Coupling Stability
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Solution Overview
Problem
Conventional motion simulators face challenges in accurately compensating for Coriolis, centrifugal, and gravity torques, leading to instability and performance issues, especially at high rotation speeds, due to their monovariable control laws that do not account for interactions between joints.
Innovation Solution
A multivariable control law is implemented, which includes a monovariable corrector block and a non-linear compensation law to estimate and compensate for disturbing torques by calculating an estimation of these torques using a dynamic model and injecting it into the control loop, allowing for real-time adaptive compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multivariable control law is implemented to explicitly account for joint interactions, then stability and performance improve, but device complexity increases
Solution Approach 1:
The control system is segmented into independent monovariable corrector blocks for each joint, where each block handles only its own joint's position and speed feedback. This segmentation simplifies individual controller design while the overall multivariable structure accounts for joint interactions through the systematic inclusion of coupling torque compensation terms in each joint's control equation.
Solution Approach 2:
The patent introduces intermediary torque compensation terms that represent the coupling effects between joints. These intermediary terms act as mediators that transmit the influence of one joint's motion to another joint's control, explicitly accounting for Coriolis, centrifugal, and gravity torques without requiring full multivariable control complexity.
2Device complexity
If monovariable control laws are used, then device complexity is reduced, but stability deteriorates at high rotation speeds
Solution Approach 1:
The control system performs preliminary compensation for Coriolis, centrifugal, and gravity torques by calculating these coupling effects in advance based on the current positions and speeds of all joints. This preliminary action removes the harmful coupling effects before they can destabilize the system, allowing monovariable correctors to maintain stability even at high rotation speeds.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor the positions and speeds of all joints and use this information to calculate and compensate for coupling torques. The feedback loop ensures that as joints move and coupling effects change, the compensation is dynamically adjusted to maintain stability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly attenuates the harmful effects of Coriolis, centrifugal, and gravity torques, improving the stability and performance of motion simulators by explicitly accounting for joint interactions and disturbances.
Implementation Method 1
These couplings are non-linear and result from Coriolis, centrifugal, and gravity torques.
Implementation Method 2
These couplings are non-linear and result from Coriolis, centrifugal, and gravity torques.
Implementation Method 3
These couplings are non-linear and result from Coriolis, centrifugal, and gravity torques.
Data Source
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AI summary
The invention relates to a method for compensating for disruptive couples for a movement simulator, said simulator including, for each axis, a monovariable correcting unit Corr(q'1) that receives a signal giving the difference between the setpoint θ r j and the measurement θ j for the corresponding axis and producing the control signal U j . According to the invention, the disruptive couples are coriolis, centrifugal and gravitational couples and furthermore a non-linear and multivariable compensating law is implemented that calculates a formula (a) estimating the disruptive couples, formula (a) being calculated on the basis of an error ε j (t) that is the control signal U j filtered by a filter H(q -1), and the simulator is modelled with a dynamic model expressing the couples in an affine way with respect to a set of base parameters ϰ according to a matrix relationship of the type: formula (b), and a subset j of base parameters, the estimation of the couples being formula (c), and, online, the αj are calculated via an iterative equation: formula (d) where ϕFj (t) = H(q-1)T(q-1)Φj(t) are the elements of the vectors formula (e) being filtered with H j Corr j G j /(1+C αj H j ), T being defined by the relationship 1 + Corr(q-1)L(q-1))⋅ T(q-1) = Corr(q-1)L(q-1) where L j (q -1) is the discreet transmittance of the transform of the product formula (f) where J j are inertias and μ1· is a positive real definite square matrix.