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PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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Quantized controller for a class of uncertain nonlinear systems with dead-zone nonlinearity.

Jitendra Kumar Jain¹, Weidong Zhang¹, Xiaocheng Liu¹

¹Department of Automation, Shanghai Jiaotong University, Shanghai, 200240, China.

ISA Transactions

|September 1, 2020

Summary

This study introduces a novel quantized controller for uncertain nonlinear systems with disturbances and dead-zone nonlinearity. The controller effectively stabilizes these complex systems, reducing computational load through disturbance bound estimation.

Keywords:

Backstepping control Dead-zone nonlinearity Nonlinear systems Quantized input Time-varying disturbance Unknown parameter

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Area of Science:

Control Engineering
Nonlinear Systems Theory
Robotics

Background:

Uncertain nonlinear systems present significant control challenges due to unknown parameters, disturbances, and actuator nonlinearities like dead-zones.
Existing control methods often struggle with high computational complexity when dealing with multiple uncertainties.

Purpose of the Study:

To design a quantized controller for a class of uncertain nonlinear systems with unknown disturbances and dead-zone nonlinearity.
To reduce computational cost by estimating the maximum upper bound of disturbances instead of individual disturbances.

Main Methods:

A novel quantized controller is designed for strict feedback nonlinear systems.
Tuning functions are developed to estimate unknown system parameters and disturbance bounds.
A backstepping technique is employed for controller and tuning function design.
Lyapunov stability theory is used to prove the controller's stability.

Main Results:

The proposed quantized controller effectively stabilizes uncertain nonlinear systems.
The method reduces computational complexity by estimating disturbance bounds.
MATLAB simulations confirm the controller's performance and stability.

Conclusions:

A computationally efficient quantized controller is presented for uncertain nonlinear systems.
The controller robustly handles unknown disturbances and dead-zone nonlinearities.
The backstepping approach combined with Lyapunov stability ensures system stabilization.