Basic Concepts of Robot control

Robot Control System Task
The task of a robot control system is to execute the planned sequence of motions and forces in the presence of unforseen errors.

Errors can arise from:
– inaccuracies in the model of the robot,
– tolerances in the workpiece,
– static friction in joints,
– mechanical compliance in linkages,
– electrical noise on transducer signals, and
– limitations in the precision of computation.

Controlled Variables
In both Cartesian and joint spaces, we require precise control of: Position, Velocity, Force and Torque.

Robot Control Techniques

Open Loop Control (Nonservo Control)
No Feedback! Basic control suitable for systems with simple loads,
Tight speed control is not required, no position or rate-of-change sensors, on each axis, there is a fixed mechanical stop to set the endpoint of the robot, its called “stop-to-stop” or “pick-and-place” systems.

The desired change in a parameter is calculated (joint angles), The actuator energy needed to achieve that change is determined, and the amount of energy is applied to the actuator. If the model is correct and there are no disturbances, the desired change is achieved.

Feedback Control Loop
Determine rotor position and/or speed from one or more sensors. Position of robot arm is monitored by a position sensor, power to the actuator is altered so that the movement of the arm conforms to the
desired path in terms of direction and/or velocity. Errors in positioning are corrected.

Feedforward Control
It is a control, where a model is used to predict how much action to take, or the amount of energy to use. It is used to predict actuator settings for processes where feedback signals are delayed and in processes where the dynamic effects of disturbances must be reduced.

Adaptive Control
This control uses feedback to update the model of the process based upon the results of previous actions. The measurements of the results of previous actions are used to adapt the process model to correct for changes in the process and errors in the model. This type of adaption corrects for errors in the model due to long-term variations in the environment but it cannot correct for dynamic changes caused by local disturbances.