Feedback Linearization + Contractive MPC: Stability Analysis / Application to a Polymerization Process

1998 ◽  
pp. 433-464
Author(s):  
Simone Loureiro Oliveira ◽  
Douglas Marcelo Merquior ◽  
Enrique Luis Lima
Keyword(s):  
Stability Analysis ◽  
Feedback Linearization ◽  
Polymerization Process ◽  
Analysis Application

Research of Nonlinear Adaptive Flight Control Using Inversible System of Neural Networks

2014 ◽  
Vol 490-491 ◽  
pp. 960-963
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Cen Rui Ma ◽  
Cong Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stability Analysis ◽  
Flight Control ◽  
Feedback Linearization ◽  
Adaptive Neural Networks ◽  
Adaptive Flight Control ◽  
The Status ◽  
Training Structure

This paper discusses training structure and procedure about inversible system of neural network. Feedback linearization and adaptive neural networks provide a powerful controller architecture. Finally, this paper surveys the status of nonlinear, and adaptive flight control, and summarizes the research being conducted in this area. A description of the controller architecture and associated stability analysis is given.

Sampled data output regulation of n-link robotic manipulator using a realizable reconstruction filter

Robotica ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 900-912 ◽  
Author(s):  
Muwahida Liaquat ◽  
Mohammad Bilal Malik
Keyword(s):  
Stability Analysis ◽  
Discrete Time ◽  
Feedback Linearization ◽  
Impulsive System ◽  
Observer Design ◽  
Control Input ◽  
Sampled Data ◽  
Control Scheme ◽  
Reconstruction Filter ◽  
The Stability

SUMMARYThis paper proposes a solution to the sampled-data regulation problem for feedback linearizable n-link robotic manipulators. The prime focus is on the development and stability analysis of the proposed control scheme in the presence of model uncertainties and external disturbances. A major constraint is the availability of sampled measurements of output signal. This leads to designing an impulsive observer for feedback linearization. The discrete-time control input is mapped into its continuous-time counterpart using a realizable reconstruction filter (RRF). The underlying control scheme relies on the sampled-data regulator theory based on the discrete-time equivalence of the plant and RRF modeled as impulsive system. This method leads to controller/observer design in discrete time. The working of the entire scheme is dependent on the stability of impulsive observer; hence a Lyapunov-based stability analysis is also included to ensure the stability of a closed-loop system. The working of the proposed scheme along with a comparison with conventional solution is presented, when applied to the control of a 3-degree-of-freedom PUMA 560 robot.

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