Mechatronic Design and Optimization Using Knowledge Based Engineering Applied to an Inherently Unstable and Unmanned Aerial Vehicle

Abstract

A novel design method for mechatronic systems, based on knowledge based engineering techniques, is proposed in this research study. The method is particularly suited for mechatronic vehicles which are inherently unstable and require control systems for stabilization. The method is implemented in a dedicated software tool in which physical entities of the product are defined as classes with attributes. Non-physical elements of the system and procedures for the design and analysis of the system are defined as functions with variables. The method has two key features. First, multiphysics simulation models and associated analysis functions are generated automatically within a multidisciplinary analysis and optimization framework. These models are not restricted to geometrical aspects for mechanical design, but also include the system architecture, dynamics, aerodynamics, electronic control systems and associated software codes. Second, for each representation of a design, a dedicated control system is developed completely automatically, based on the multiphysics simulation model, using model inversion control. These two features make it possible to analyse the dynamics and performance of inherently unstable mechatronic vehicles already in the early design phases when the vehicle is still subject to large configuration design changes. The method is demonstrated for the design of a multirotor unmanned aerial vehicle. Thirty thousand possible design solutions are evaluated by the system without manual interference. For each design, a dedicated control system is created and five flight test maneuvers are simulated in order to assess the aircraft performance and flying qualities. A global optimization process is applied for two conflicting requirements and the process is convergent at two optimum solutions.

Design Automation and Optimization Methodology for Electric Multicopter UAVs

“…In practice, there is no uniform standard to evaluate the design of a multicopter. According to [19,20], the multicopter design optimization problem is essentially a multiobjective optimization problem, so an "optimal design" should fully consider all factors concerned by designers and customers. Therefore, an objective function J = f J (Θ out ) is proposed in this paper to evaluate the obtained multicopter designs and find the optimal Θ * out .…”

“…Therefore, an objective function J = f J (Θ out ) is proposed in this paper to evaluate the obtained multicopter designs and find the optimal Θ * out . Compared to the objective functions in [19,20], two improvements are introduced in this paper. First, the normalization operation is applied to each evaluation index, which makes it easier to determine the weight factor according to the preferences of designers.…”

“…In [19], the design optimization problem was solved based on discrete-integer and continuous variables with the objectives of efficiency and flight time. In [20], based on knowledge-based engineering techniques, a design method was proposed to find a multicopter design with the minimum cost and maximum payload.…”

Improvement of Multidisciplinary Integration in Design of Complex Systems by Implementing Knowledge-Based Engineering

“…La Rocca and van Tooren [15] develop conceptual aircraft design applications based on KBE. Tian and Voskuijl [16] integrate the knowledge related to Electronic systems into the application developed by La Rocca and van Tooren to support the conceptual design of mechatronic systems. The literature review shows that most of the studies on those design methodologies based on KBE focus on the conceptual design phase.…”

Efficient sizing and optimization of multirotor drones based on scaling laws and similarity models

“…Such approaches are mandatory for aerospace vehicles for instance fixed-wings [4], rotorcraft [5] or tilted-rotors [6]. To assist the designer, numerical methods have to be used to determine the best feasible configuration and can use different methods such as statistical-based [7] or optimizationbased [8,9] techniques.…”