Inverse optimal adaptive output feedback control of a class of Euler–Lagrange systems: A nonlinear filter based approach

Author(s):  
Orhan Aksoy ◽  
Erkan Zergeroglu ◽  
Enver Tatlicioglu

In this paper, we present an inverse optimal tracking controller for a class of Euler–-Lagrange systems having uncertainties in their dynamical terms under the restriction that only the output state ( i.e. position for robotic systems) is available for measurement. Specifically, a nonlinear filter is used to generate a velocity substitute, then a controller formulation ensuring a globally asymptotically stable closed-loop system while minimizing a performance index despite the presence of parametric uncertainty, is proposed. The stability proof is established using a Lyapunov analysis of the system with proposed optimal output feedback controller. Inverse optimality is derived via designing a meaningful cost function utilizing the control Lyapunov function. Numerical simulations are presented to illustrate the viability and performance of the derived controller.

Author(s):  
Mounir Hammouche ◽  
Philippe Lutz ◽  
Micky Rakotondrabe

The problem of robust and optimal output feedback design for interval state-space systems is addressed in this paper. Indeed, an algorithm based on set inversion via interval analysis (SIVIA) combined with interval eigenvalues computation and eigenvalues clustering techniques is proposed to seek for a set of robust gains. This recursive SIVIA-based algorithm allows to approximate with subpaving the set solutions [K] that satisfy the inclusion of the eigenvalues of the closed-loop system in a desired region in the complex plane. Moreover, the LQ tracker design is employed to find from the set solutions [K] the optimal solution that minimizes the inputs/outputs energy and ensures the best behaviors of the closed-loop system. Finally, the effectiveness of the algorithm is illustrated by a real experimentation on a piezoelectric tube actuator.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Cheng ◽  
Quanxin Zhu ◽  
Zhangsong Yao

We address the problem of the globally asymptotic stability for a class of stochastic nonlinear systems with the output feedback control. By using the backstepping design method, a novel dynamic output feedback controller is designed to ensure that the stochastic nonlinear closed-loop system is globally asymptotically stable in probability. Our way is different from the traditional mathematical induction method. Indeed, we develop a new method to study the globally asymptotic stability by introducing a series of specific inequalities. Moreover, an example and its simulations are given to illustrate the theoretical result.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiao Han ◽  
Zhijian Ji ◽  
Qingyuan Qi

This paper is concerned with the optimal output feedback control problem for networked control systems (NCSs) with Markovian packet losses. In this paper, the packet losses occur both between the sensor and controller and between the controller and actuator. Moreover, the packet loss channels are described with two-state Markov chains. Since the precise state information cannot be obtained, thus an optimal recursive estimator is designed. Furthermore, by adopting the dynamic programming approach, we derive the optimal output feedback control, which is based on the solution to a given modified Riccati equation. The obtained results can be seen as an important implementation of the control theory for NCSs with unreliable communication channels.


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