On the equivalence between P+DOB and set point weighted PI controllers for velocity control of servodrives under load disturbances

For artificial legs that are used in legged robots, exoskeletons, and prostheses, it suffices to achieve velocity regulation at a few key instants of swing rather than tight trajectory tracking. Here, we advertise an event-based, intermittent, discrete controller to enable set-point regulation for problems that are traditionally posed as trajectory following. We measure the system state at prior-chosen instants known as events (e.g., vertically downward position), and we turn on the controller intermittently based on the regulation errors at the set point. The controller is truly discrete, as these measurements and controls occur at the time scale of the system to be controlled. To enable set-point regulation in the presence of uncertainty, we use the errors to tune the model parameters. We demonstrate the method in the velocity control of an artificial leg, a simple pendulum, with up to 50% mass uncertainty. Starting with a 100% regulation error, we achieve velocity regulation of up to 10% in about five swings with only one measurement per swing.

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Design of adaptive digital set-point tracking PI controllers incorporating recursive step-response matrix identifiers for gas turbines

10.2514/6.1986-2156 ◽

1986 ◽

Cited By ~ 1

Author(s):

A. JONES ◽

B. PORTER

Keyword(s):

Gas Turbines ◽

Step Response ◽

Response Matrix ◽

Set Point ◽

Point Tracking ◽

Pi Controllers

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Application of neural models as controllers in mobile robot velocity control loop

Journal of Electrical Engineering ◽

10.1515/jee-2017-0005 ◽

2017 ◽

Vol 68 (1) ◽

pp. 39-46 ◽

Cited By ~ 4

Author(s):

Jakub Cerkala ◽

Anna Jadlovska

Keyword(s):

Mobile Robot ◽

Simulation Experiment ◽

Inverse Dynamics ◽

Dynamic Properties ◽

Neural Model ◽

Input Voltage ◽

Velocity Control ◽

Neural Models ◽

Control Task ◽

Pi Controllers

Abstract This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

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