Yuekun Chen and Yousef Sardahi and Salam Hajjar and Christopher Greer
MultiObjective Optimal Design of a Cascade Control System for a Class of Underactuated Mechanical Systems
196 - 202
2020
14
5
International Journal of Mechanical and Mechatronics Engineering
https://publications.waset.org/pdf/10011222
https://publications.waset.org/vol/161
World Academy of Science, Engineering and Technology
This paper presents a multiobjective optimal design of
a cascade control system for an underactuated mechanical system.
Cascade control structures usually include two control algorithms
(inner and outer). To design such a control system properly, the
following conflicting objectives should be considered at the same
time 1) the inner closedloop control must be faster than the outer
one, 2) the inner loop should fast reject any disturbance and prevent
it from propagating to the outer loop, 3) the controlled system
should be insensitive to measurement noise, and 4) the controlled
system should be driven by optimal energy. Such a control problem
can be formulated as a multiobjective optimization problem such
that the optimal tradeoffs among these design goals are found.
To authors best knowledge, such a problem has not been studied
in multiobjective settings so far. In this work, an underactuated
mechanical system consisting of a rotary servo motor and a ball
and beam is used for the computer simulations, the setup parameters
of the inner and outer control systems are tuned by NSGAII
(Nondominated Sorting Genetic Algorithm), and the dominancy
concept is used to find the optimal design points. The solution of
this problem is not a single optimal cascade control, but rather a set
of optimal cascade controllers (called Pareto set) which represent the
optimal tradeoffs among the selected design criteria. The function
evaluation of the Pareto set is called the Pareto front. The solution
set is introduced to the decisionmaker who can choose any point
to implement. The simulation results in terms of Pareto front and
time responses to external signals show the competing nature among
the design objectives. The presented study may become the basis for
multiobjective optimal design of multiloop control systems.
Open Science Index 161, 2020