Software for control and estimation in Industrial Robotics  

Despite vast progress in the field of industrial robotics, in several applications PID control remains in use thus causing problems of stability and robustness while also requiring retuning at every change of operating conditions. To solve the control problem of industrial robots in a conclusive manner, elaborated control software for control and estimation in industrial robotic systems systems is developed by the Unit of Industrial Automation, mainly in Matlab, Visual Basic and C++.

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Flatness-based control and filtering of 2-DOF industrial robot


• Kalman Filter-based control of the manipulator - state x1
  
  
• Kalman Filter-based control of the manipulator - state x2
  
  
• Kalman Filter-based control of the manipulator - state x3
  
  
• Kalman Filter-based control of the manipulator - state x4
  
  
• Motion of the 2DOF manipulator in its workspace
  
  

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Flatness-based adaptive control of 2-DOF industrial robot


• Adaptive control of the manipulator - state x1
  
  
• Adaptive control of the manipulator - state x2
  
  
• Adaptive control of the manipulator - state x3
  
  
• Adaptive control of the manipulator - state x4
  
  
• Motion of the 2DOF manipulator in its workspace
  
  

Method analyzed in [4] G.G. Rigatos, A differential flatness theory approach to observer-based adaptive fuzzy control of MIMO nonlinear dynamical systems, Nonlinear Dynamics, Springer, 2014 download

Flatness-based adaptive control of 2-DOF industrial robot with the use of state observer


• Observer-based adaptive control of the manipulator - state x1
  
  
• Observer-based adaptive control of the manipulator - state x2
  
  
• Observer-based adaptive control of the manipulator - state x3
  
  
• Observer-based adaptive control of the manipulator - state x4
  
  
• Motion of the 2DOF manipulator in its workspace
  
  

Method analyzed in [10] G.G. Rigatos, Model-based and model-free control of flexible-link robots: a comparison between representative methods, Applied Mathematical Modeling, Elsevier, vol. 33, no. 10, pp. 3906-3925, 2009, download

Energy-based control of 2-DOF flexilbe-link robot


• Energy-based control of a flexible-link manipulator - state q1
  
  
• Energy-based control of a flexible-link manipulator - state d/dt q1
  
  
• Energy-based control of a flexible-link manipulator - state q2
  
  
• Energy-based control of a flexible-link manipulator - state d/dt q2
  
  
• Energy-based control of a flexible-link manipulator - vibration mode v11
  
  
• Energy-based control of a flexible-link manipulator - vibration mode v12
  
  
• Energy-based control of a flexible-link manipulator - vibration mode v21
  
  
• Energy-based control of a flexible-link manipulator - vibration mode v22
  
  
• Motion of the 2DOF manipulator in its workspace
  
  

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Kalman Filter-based control of 2-DOF flexilbe-link robot


• Kalman Filter-based control of a flexible-link manipulator - state q1
  
  
• Kalman Filter-based control of a flexible-link manipulator - state d/dt q1
  
  
• Kalman Filter-based control of a flexible-link manipulator - state q2
  
  
• Kalman Filter-based control of a flexible-link manipulator - state d/dt q2
  
  
• Kalman Filter-based control of a flexible-link manipulator - vibration mode v11
  
  
• Kalman Filter-based control of a flexible-link manipulator - vibration mode v12
  
  
• Kalman Filter-based control of a flexible-link manipulator - vibration mode v21
  
  
• Kalman Filter-based control of a flexible-link manipulator - vibration mode v22
  
  
• Motion of the 2DOF manipulator in its workspace
  
  

Method analyzed in [3] Control and disturbances compensation in underactuated robotic systems using the Derivative-free nonlinear Kalman Filter, Robotica, Cambridge University Press, 2015 download

Kalman Filter-based control of an underactuated robotic manipulator


• Kalman Filter-based control of an underactuated robotic manipulator - state q1
  
  
• Kalman Filter-based control of an underactuated robotic manipulator - state d/dt q1
  
  
• Kalman Filter-based control of an underactuated robotic manipulator - stte q3
  
  
• Kalman Filter-based control of an underactuated robotic manipulator - state d/dt q3
  
  
• Kalman Filter-based control of an underactuated robotic manipulator - control input u
  
  
• Kalman Filter-based control of an underactuated robotic manipulator - estimation of external disturbances
  
  

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Flatness-based adaptive control of an underactuated robotic manipulator


• Flatness-based adaptive control of an underactuated robotic manipulator - state q1
  
  
• Flatness-based adaptive control of an underactuated robotic manipulator - state d/dt q1
  
  
• Flatness-based adaptive control of an underactuated robotic manipulator - stte q3
  
  
• Flatness-based adaptive control of an underactuated robotic manipulator - state d/dt q3
  
  
• Flatness-based adaptive control of an underactuated robotic manipulator - control input u
  
  

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Flatness-based adaptive control of MEMS


• Flatness_based control of MEMS - state x1
  
  
• Flatness_based control of MEMS - state x2
  
  
• Flatness_based control of MEMS - state x3
  
  
• Flatness_based control of MEMS - control input u
  
  

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Kalman Filter-based force control for robotic manipulators


• Kalman Filter-based force control - state x1
  
  
• Kalman Filter-based force control - state x2
  
  
• Kalman Filter-based force control - state x3
  
  
• Kalman Filter-based force control - control input u
  
  
• Kalman Filter-based force control - estimation of external disturbances
  
  

Stabilization of nonlinear wave dynamics with distributed control


• Stabilization of wave dynamics with distributed control - grid point x10
  
  
• Stabilization of wave dynamics with distributed control - grid point x20
  
  
• Stabilization of wave dynamics with distributed control - grid point x21
  
  
• Stabilization of wave dynamics with distributed control - grid point x30
  
  
• Stabilization of wave dynamics with distributed control - grid point x40
  
  
• Stabilization of wave dynamics with distributed control - controlled PDE
  
  

Method analyzed in [2] G. Rigatos, Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems, Springer, 2015 (monograph) download

Stabilization of nonlinear wave dynamics with boundary control


• Stabilization of wave dynamics with boundary control - grid point x1
  
  
• Stabilization of wave dynamics with boundary control - grid point x2
  
  
• Stabilization of wave dynamics with boundary control - grid point x3
  
  
• Stabilization of wave dynamics with boundary control - grid point x4
  
  
• Stabilization of wave dynamics with boundary control - controlled PDE
  
  

Nonlinear feedback control of a 1-DOF robotic manipulator in VB


• Lab exercise on nonlinear robot control
  
  
• Visual Basic code about 1-DOF robot nonlinear control