Constrained incremental predictive controller design for a flexible joint robot
Related Experiment Video
Updated: Jun 24, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
Published on: November 6, 2015
Nemat Ollah Ghahramani1, Farzad Towhidkhah
1Department of Aerospace Engineering, Amirkabir University of Technology, Tehran, Iran.
Abstract:
In this paper, an improved predictive control algorithm for controlling a typical nonlinear flexible-joint robot (FJR) with input constraint is proposed. The receding horizon algorithm, called generalized incremental predictive control (GIPC), utilizes both present and previous states rather than present states only. The GIPC algorithm includes the weighted difference of the current and the previous states and the summation of the control action increments. In order to illustrate the effectiveness of the proposed control strategy, it is implemented to the FJR and the results are compared with those of generalized predictive control (GPC). It is demonstrated that the proposed GIPC algorithm is more robust than the standard GPC method. Furthermore, the constrained GIPC algorithm using the quadratic programming removes instabilities caused by actuator saturation.
Related Concept Videos
One-Degree-of-Freedom System
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
Controller Configurations
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...
PD Controller: Design
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
PID Controller
PI Controller: Design
Kinematic Equations: Problem Solving
