A high gain nonlinear observer: application to the control of an unstable nonlinear process

Abstract This paper presents an averaged state model and the design of nonlinear observers for an on/off pneumatic actuator. The actuator is composed of two chambers and four on/off solenoid valves. The elaborated averaged state model has the advantage of using only one continuous input instead of four binary inputs. Based on this new model, a high gain observer and a sliding mode observer are designed using the piston position and the pressure measurements in one of the chambers. Finally, their closed-loop performances are verified and compared on an experimental benchmark.

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Unknown Inputs Nonlinear Observer for an Activated Sludge Process

Mathematical Problems in Engineering ◽

10.1155/2018/1382914 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Feten Smida ◽

Taoufik Ladhari ◽

Salim Hadj Saïd ◽

Faouzi M’sahli

Keyword(s):

Activated Sludge ◽

High Gain ◽

The Other ◽

Nonlinear Observer ◽

Dynamics Simulation ◽

Activated Sludge Process ◽

Unknown Inputs ◽

Simulation Results ◽

Economic Concentration ◽

Substrate Concentrations

This paper deals with the jointly estimation problem of unknown inputs and nonmeasured states of one altering aerated activated sludge process (ASP). In order to provide accurate and economic concentration measures during aerobic and anoxic phases, a cascade high gain observer (HGO) approach is developed. Only two concentrations are available; the other process’s states are assumed unavailable. The observer converges asymptotically and it leads to a good estimation of the unavailable states which are the ammonia and substrate concentration, as well as a quite reconstruction of the unknown inputs, which are the influent ammonia and the influent substrate concentrations. To highlight the efficiency of the proposed HGO with this MIMO system’s dynamics, simulation results are validated with experimental data.

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Neural-Network Based On-Line Adaptation of Model Predictive Controller for Dynamic Systems With Uncertain Behavior

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2005-82609 ◽

2005 ◽

Author(s):

Marco E. Sanjuan

Keyword(s):

Neural Network ◽

Controller Design ◽

Linear Equations ◽

Nonlinear Process ◽

High Gain ◽

Industrial Applications ◽

Random Allocation ◽

Dynamic Matrix ◽

Highly Nonlinear ◽

On Line

Model-based controllers have positioned themselves in industrial applications, working mainly on top of a layer of PID controllers. Their implementation takes an important amount of time because of the required PID tuning and the model characterization/identification. This paper presents a strategy to perform on-line adaptation for the dynamic matrix coefficients in a DMC controller. Based on the observed PH (Prediction Horizon) elements of the response and controller signal vectors, and based on a non-residing control horizon controller design, the direct control problem is reformulated using the full-effect dynamic matrix (PHxPH) as an unknown. Data is collected and used in two directions: training a RAWN Network (Random Allocation Weight Neural Network), to describe recently observed process behavior, and to solve a least-squares problem for a set of linear equations where the unknowns are the characteristic response coefficients. The paper presents the effect of both approaches, illustrating the adaptation algorithms operation in a highly nonlinear process where the controller is designed in a low-gain region. Then the process operating condition is shifted so that it moves to a high-gain region to observe controller response.

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High-Gain Nonlinear Observer Using System State Augmentation

2020 59th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc42340.2020.9303930 ◽

2020 ◽

Author(s):

D. Bouhadjra ◽

A. Zemouche ◽

A. Alessandri ◽

P. Bagnerini

Keyword(s):

High Gain ◽

Nonlinear Observer ◽

System State ◽

State Augmentation

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A Fractional High-Gain Nonlinear Observer Design—Application for Rivers Environmental Monitoring Model

Mathematical and Computational Applications ◽

10.3390/mca25030044 ◽

2020 ◽

Vol 25 (3) ◽

pp. 44

Author(s):

Abraham Efraim Rodriguez-Mata ◽

Yaneth Bustos-Terrones ◽

Victor Gonzalez-Huitrón ◽

Pablo Antonio Lopéz-Peréz ◽

Omar Hernández-González ◽

...

Keyword(s):

Fractional Order ◽

Oxygen Demand ◽

Environmental Water ◽

High Gain ◽

Observer Design ◽

Nonlinear Observer ◽

Distribution Models ◽

Luenberger Observer ◽

Monitoring Model ◽

Numerical Studies

The deterioration of current environmental water sources has led to the need to find ways to monitor water quality conditions. In this paper, we propose the use of Streeter–Phelps contaminant distribution models and state estimation techniques (observer) to be able to estimate variables that are very difficult to measure in rivers with online sensors, such as Biochemical Oxygen Demand (BOD). We propose the design of a novel Fractional Order High Gain Observer (FOHO) and consider the use of Lyapunov convergence functions to demonstrate stability, as it is compared to classical extended Luenberger Observer published in the literature, to study the convergence in BOD estimation in rivers. The proposed methodology was used to estimated Dissolved oxygen (DO) and BOD monitoring of River Culiacan, Sinaloa, Mexico. The use of fractional order in high-gain observers has a very effective effect on BOD estimation performance, as shown by our numerical studies. The theoretical results have shown that robust observer design can help solve problems in estimating complex variables.

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