We model the system you need to control and apply suitable control and filtering techniques to achieve the required accuracy.
If you need to control a vehicle, industrial machinery or any other device over a trajectory, Freelance Robotics can do it for you.

What is Control

Machine operation requires an understanding of both the machine and it’s In the realm of automation, understanding the environment is crucial. Therefore, the initial step involves modelling the structure and physical surroundings that necessitate adaptation. Subsequently, the machine can effectively respond to commands and feedback, enabling precise control over object manipulation or position adjustment while factoring in considerations like speed and acceleration. Notable points on the topic of control and filtering relevant to operational development are included below.

The key to effective control is the feedback of information to allow correction of initial “blind” commands. An external measurement
of the environment is taken and informs the controller of the distance from real position, and speed and acceleration needed to reach the target.

System reliability and efficiency are important measures for machine operation. This is assessed by stability and time response. Output here describes how quickly and smoothly a controller reaches either the commanded or, in case of error, the current position.

Filtering is also central to control knowledge. Filtering eliminates unwanted noise, bias or scaling factors from the feedback measurement of the environment to obtain more relevant and accurate information.

Finally, observation knowledge is a useful measure. Observation knowledge provides an estimate of the system model by observing controller behaviour and environmental feedback. In this way, the controller can be modified either in real time or later, to more accurately perform the command.

Control and Filtering Methods Used

Control Techniques:

  • PID Controller System
  • Quadratic
  • Force Feedback
  • Non Linear Controller

Noise Filtering and Sensor Merging Technics:

  • Kalman Filter
  • IIR Filter
  • FIR Filter
  • Particles Filter
  • Electrical Enclosure with Control System
    PID and Filtering Integrated in PLC

Control and Filtering Application Examples:

  • Control Applications for Terrestrial, Aerial and Marine Vehicles
  • Motor and Industrial Machine Control
  • Mapping or SLAM (Simultaneous Localisation and Mapping)
  • Dead Reckoning (Continue Trajectory Without Global Positioning)
Control System
Control Stimulator