Model-Free Control Approach of a Three-Tank System Using an Adaptive-Based Control

2017 ◽  
Author(s):  
Elmira Madadi ◽  
Yao Dong ◽  
Dirk Söffker
Keyword(s):  
Tracking Error ◽  
Identification Problem ◽  
Dynamical Structure ◽  
Modified Model ◽  
Control Approach ◽  
Model Free ◽  
Tank System ◽  
Model Free Control ◽  
Complex Nonlinear Systems ◽  
Error Optimization

For improving the dynamics of systems in the last decades model-based control design approaches are continuously developed. The task to design an accurate model is the most relevant and related task for control engineers, which is time consuming and difficult if in the case of complex nonlinear systems a complex modeling or identification problem arises. For this reason model-free control methods become attractive as alternative to avoid modeling. This contribution focuses on design methods of a model-free adaptive-based controller and modified model-free adaptive-based controller. Modified approach is based on the same adaptive model-free control algorithm performing tracking error optimization. Both approaches are designed for non-linear systems with uncertainties and in the presence of disturbances in order to assure suitable performance as well as robustness against unknown inputs. Using this approach, the controller requires neither the information about the systems dynamical structure nor the knowledge about systems physical behaviors. The task is solved using only the system outputs and inputs, which are measurable. The effectiveness of the proposed method is validated by experiments using a three-tank system.

Towards an Adaptive Control Concept for Unknown Nonlinear MIMO Systems

2018 ◽  
Author(s):  
Elmira Madadi ◽  
Dirk Söffker
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Control Method ◽  
Mimo Systems ◽  
Tracking Error ◽  
Underlying Structure ◽  
Unknown Parameters ◽  
Modified Model ◽  
Model Free ◽  
Model Free Control

The design of an accurate model often appears as the most challenging tasks for control engineers especially focusing to the control of nonlinear systems with unknown parameters or effects to be identified in parallel. For this reason, development of model-free control methods is of increasing importance. The class of model-free control approaches is defined by the non-use of any knowledge about the underlying structure and/or related parameters of the dynamical system. Therefore the major criteria to evaluate model-free control performance are aspects regarding robustness against unknown inputs and disturbances to achieve a suitable tracking performance including ensuring stability. Consequently it is assumed that the system plant model to be controlled is unknown, only the inputs and outputs are used as measurements. In this contribution a modified model-free adaptive approach is given as the extended version of existing model-free adaptive control to improve the performance according to the tracking error at each sample time. Using modified model-free adaptive controller, the control goal can be achieved efficiently without an individual control design process for different kinds unknown nonlinear systems. The main contribution of this paper is to extend the modified model-free adaptive control method to unknown nonlinear multi-input multi-output (MIMO) systems. A numerical example is shown to demonstrate the successful application and performance of this method.

scholarly journals Model-Free Optimized Tracking Control Heuristic

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 49 ◽  
Author(s):  
Ning Wang ◽  
Mohammed Abouheaf ◽  
Wail Gueaieb ◽  
Nabil Nahas
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Reference Signal ◽  
Control Algorithms ◽  
Learning Tools ◽  
Control Effort ◽  
Control Loops ◽  
Model Free ◽  
Architectural Framework ◽  
Model Free Control

Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

scholarly journals Model-free based control of a HIV/AIDS prevention model

2021 ◽  
Vol 19 (1) ◽  
pp. 759-774
Author(s):  
Loïc Michel ◽  
◽  
Cristiana J. Silva ◽  
Delfim F. M. Torres ◽  
◽  
...  
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
The State ◽  
State Solution ◽  
Control Constraints ◽  
Controlled System ◽  
Control Approach ◽  
Model Free ◽  
Model Free Control ◽  
Optimal Control Approach

<abstract><p>Controlling an epidemiological model is often performed using optimal control theory techniques for which the solution depends on the equations of the controlled system, objective functional and possible state and/or control constraints. In this paper, we propose a model-free control approach based on an algorithm that operates in 'real-time' and drives the state solution according to a direct feedback on the state solution that is aimed to be minimized, and without knowing explicitly the equations of the controlled system. We consider a concrete epidemic problem of minimizing the number of HIV infected individuals, through the preventive measure <italic>pre-exposure prophylaxis (PrEP)</italic> given to susceptible individuals. The solutions must satisfy control and mixed state-control constraints that represent the limitations on PrEP implementation. Our model-free based control algorithm allows to close the loop between the number of infected individuals with HIV and the supply of PrEP medication 'in real time', in such a manner that the number of infected individuals is asymptotically reduced and the number of individuals under PrEP medication remains below a fixed constant value. We prove the efficiency of our approach and compare the model-free control solutions with the ones obtained using a classical optimal control approach via Pontryagin maximum principle. The performed numerical simulations allow us to conclude that the model-free based control strategy highlights new and interesting performances compared with the classical optimal control approach.</p></abstract>

scholarly journals Fixed-wing UAV with transitioning flight capabilities : Model-Based or Model-Free Control approach? A preliminary study

2018 ◽  
Author(s):  
Jacson M. O. Barth ◽  
Jean-Philippe Condomines ◽  
Murat Bronz ◽  
Leandro R. Lustosa ◽  
Jean-Marc Moschetta ◽  
...  
Keyword(s):  
Control Approach ◽  
Model Based ◽  
Model Free ◽  
Model Free Control ◽  
Preliminary Study

Real-time application of a fuzzy adaptive control to one level in a three-tank system

2018 ◽  
Vol 232 (7) ◽  
pp. 845-856 ◽  
Author(s):  
Mohamed Bahita ◽  
Khaled Belarbi
Keyword(s):  
Real Time ◽  
Control Method ◽  
Tracking Error ◽  
Reference Signal ◽  
Control Signal ◽  
Inference System ◽  
Control Error ◽  
Model Free ◽  
Tank System ◽  
Takagi Sugeno

In this experimental work, a fuzzy combined control method has been described and applied to test the ability of the artificial intelligence technique in controlling nonlinear systems in real-time applications. A direct feedback linearization ideal control law is approximated with a Takagi–Sugeno fuzzy inference system. The adaptation law of the Takagi–Sugeno controller parameters is computed based on a fuzzy approximation term of the control error. This approximation is computed using a Mamdani fuzzy system. The experiment is applied on one level in a three-tank system and a test of the controller ability against perturbations is considered. The obtained results were compared to those leaded by a classical proportional–integral controller. The basic idea of this work is the use of the control error (between the actual control signal and the perfect control signal) instead of the tracking error (between the output of the one level in a three-tank system and the reference signal). As our approach is model free, that is, we do not use the mathematical model of the three-tank system, in the application part of this work, the output of the constructed Takagi–Sugeno fuzzy controller is injected to the real three-tank system.

scholarly journals First Steps Toward a Simple but Efficient Model-free Control Synthesis for Variable-speed Wind Turbines

2021 ◽  
Vol 14 ◽  
pp. 1181-1191
Author(s):  
Frederic Lafont ◽  
Jean-Francois Balmat ◽  
Cedric Join ◽  
Michel Fliess
Keyword(s):  
Computer Simulations ◽  
Wind Turbines ◽  
Control Synthesis ◽  
Variable Speed ◽  
Control Approach ◽  
Proportional Integral ◽  
Model Free ◽  
Model Free Control ◽  
Intelligent Controllers

In Although variable-speed three-blade wind turbines are nowadays quite popular, their control remains a challenging task. We propose a new easily implementable model-free control approach with the corresponding intelligent controllers. Several convincing computer simulations, including some fault accommodations, shows that model-free controllers are more efficient and robust than classic proportional-integral controllers.

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