Under-Actuated Aircraft Actuator Control Under Failure Constraints
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Solution Overview
Problem
Existing actuator systems, such as multiactuator aerial vehicles, face challenges in allocating control tasks when actuator malfunctions or failures occur, leading to incomplete fulfillment of desired control tasks due to degradation of control authority.
Innovation Solution
A method for controlling actuator systems involving the computation of actual control inputs using an inverse allocation matrix, prioritization of control components, and state feedback control to manage pseudo control inputs within physical actuator limits, ensuring continued system functionality despite actuator failures or malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If control tasks are allocated to multiple actuators in an overly determined actuator system, then the system can achieve given tasks through multiple possibilities, but when actuator malfunction or failure occurs, the control authority degrades and not all desired control tasks can be fulfilled
Solution Approach 1:
The control allocation method dynamically adjusts the distribution of control tasks among actuators based on real-time system state and actuator availability. When actuator failures occur, the allocation automatically reconfigures to maintain control authority for critical tasks, transitioning from a static allocation to a dynamic adaptive allocation that responds to changing system conditions.
Solution Approach 2:
The invention changes the control allocation parameters (control inputs to actuators) based on the system dynamics equation and actuator status. By computing pseudo-control inputs and transforming them to actual control inputs using the allocation equation, the system adapts parameter values to maintain effectiveness despite actuator failures or malfunctions.
2Extent of automation
If the system uses state feedback control to compute desired pseudo control input, then it can recurrently determine control inputs based on current system state, but when the desired pseudo control input exceeds maximum pseudo control input based on actuator physical capacity, control saturation occurs
Solution Approach 1:
The system employs state feedback control where the desired pseudo-control input is computed recurrently based on the current system state through the system dynamics equation. This feedback mechanism continuously monitors actuator capacity and adjusts the desired control input to ensure it remains within feasible limits, preventing saturation and maintaining reliable control.
Solution Approach 2:
When the computed desired pseudo-control input exceeds the maximum capacity, the system applies partial action by limiting the control input to the maximum feasible value. This prevents excessive control commands that would saturate actuators, ensuring that control inputs remain within physical capacity limits while still providing effective control within available actuator capabilities.
Data Source
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AI summary
We propose a method of operating an under-actuated actuator system comprising a plurality of actuators (3), preferably for operating a multiactuator aerial vehicle (1), wherein said actuators (3) are preferably devised as individual propulsion units of the multiactuator aerial vehicle (1), each one of the actuators having a maximum physical capacity umax, the method comprising: controlling the actuators (3) by with an actual control input u∈Rk computed from an allocation equation u = D-1up, wherein D-1 is an inverse allocation matrix and up∈Rm is a pseudo control input defined by a system dynamics equation M(x)ẍ + c(x,ẋ) + g(x) + G(x)up = fext, wherein x∈Rn is an n-dimensional configuration vector of the system, Mx∈Rn×n is a state dependent generalized moment of inertia, cxx˙∈ Rn are state dependent Coriolis force, gx∈Rn are gravitational forces and fext∈ Rn are external forces and torques, and Gx∈Rn×m is a control input matrix which contains the information of under-actuation, where the system is said to be under-actuated if Rank (G(x)) < n or both under-actuated and overly determined in case of k > m > n and Rank (G(x)D) < n, with k,m,n∈N; using state feedback control to recurrently compute a desired pseudo control input up from said system dynamics equation; if at least one component of said desired pseudo control input up is greater than a corresponding maximum pseudo control input upmax that can be generated based on said actuator maximum physical capacity umax and based on positions (or characteristics and configurations) of the actuators within the system, prioritizing at least one of the components of up over the other components of up; and a) by means of a prioritizing algorithm, solving said allocation equation for said at least one prioritized component of up before solving said allocation equation for the remaining components of up; or b) during state feedback control, adjusting a limiting value of said at least one prioritized component of up by an amount, while keeping respective limiting values of the remaining components of up essentially constant.