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Author : Filip Dymczyk

Project aim was to:

  • practice C++17 skills as well as OOP, design patterns and best practices,
  • increase knowledge about the behavior of dynamical control systems through their logic implementation,
  • learn to validate components with the use of GoogleTest framework,
  • incorporate responses visualization through Matplotlibcpp,
  • design a GUI using the Qt library and connect it to the dynamical systems simulation.

Logic features:

  • support of two representations of dynamical objects:
    • differential equation (SISO), e.g. $\dot{x}$ = $-a_1 x + a_2 u$,
    • state space (MIMO): $$\dot{x} = Ax + Bu \ y = Cx + Du $$
  • support of two controller types:
    • PID - with possible derivative input low pass filtering,
    • Bang-Bang.
  • possibility of simulating measurement noise on the object output,
  • ability to create whole control loops, configured as open or closed ones with or without controllers with a selected object representation,
  • different supported input signal types, such as:
    • Heaviside,
    • Ramp,
    • Rectangle,
    • Sine Wave,
    • Pulse Wave.
  • additionally, an experimental feature of PID Tuner using Recursive Linear Regression was introduced (requires further validation).

There are plans to expand the control logic features with:

  • different integration (solvers) methods - for now only the simplest forward Euler method is utilized,
  • support of discretization of continuous objects and simulating their behavior taking the sampling time into account,
  • other types of controllers, e.g. LQR,
  • introducing of observers and/or estimators, such as Kalman filter,
  • output or full state feedback,
  • pole-placing functionalities, e.g. root-locus, regular pole-placement or using optimization (LQ problem).

GUI features:

  • option to select object representation and entering parameters/matrices in Matlab-like syntax,
  • possibility to simulate it's response on the previously mentioned input signal types with an ability to change their parameters,
  • option to create more advanced control loops (open/closed) with a selected controller type (with changeable parameters),
  • possibility to plot control signal value (optional), simulate measurement noise with a tunable standard deviation and low pass filter the PID derivative input with desired coefficient,
  • ability to specify operation time and time step.
  • added experimental PID Tuner using RLS with tunable parameters (requires further validation).