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.

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.

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control 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.

The model-free adaptive cross-coupled control for two-dimensional linear motor

2019 ◽  
Vol 42 (5) ◽  
pp. 1059-1069
Author(s):  
Baolin Zhang ◽  
Rongmin Cao ◽  
Zhongsheng Hou
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Error Estimation ◽  
Tracking Error ◽  
Estimation Method ◽  
Linear Motor ◽  
Experimental Comparison ◽  
Contour Error ◽  
Two Dimensional ◽  
Model Free

In order to improve the contour error accuracy of two-dimensional linear motor, an improved cross-coupled control (CCC) scheme combining real-time contour error estimation and model-free adaptive control (MFAC) is proposed. The real-time contour error estimation method is based on CCC theory and coordinate transformation idea. It can accurately determine the contour error point of regular contour and avoid the influence of tracking error on the contour error. At the same time, for the design of two-axis error controller, only the input and output data generated by two-dimensional linear motor in reciprocating motion are used to design a multiple input multiple output-model-free adaptive control (MIMO-MFAC) algorithm, this algorithm avoids the dependence on accurate mathematical model and reduces the control difficulty. The experimental comparison showed that the proposed method improves the system tracking accuracy and contour accuracy, and verifies the proposed method correctness and effectiveness.

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.

Constrained Tracking Control by Gain-Scheduled Feedback With Optimal State Resets: A General Servo Problem and an Online Optimization Method

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Nobutaka Wada ◽  
Hidekazu Miyahara ◽  
Masami Saeki
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Optimization Problem ◽  
Control Method ◽  
Actuator Saturation ◽  
Tracking Error ◽  
Reference Signal ◽  
Optimal Solution ◽  
Optimization Method ◽  
Experimental Result

In this paper, a tracking control problem for discrete-time linear systems with actuator saturation is addressed. The reference signal is assumed to be generated by an external dynamics. First, a design condition of a controller parameterized by a single scheduling parameter is introduced. The controller includes a servo compensator to achieve zero steady-state error. Then, a control algorithm that guarantees closed-loop stability and makes the tracking error converge to zero is given. In the control algorithm, the controller state as well as the scheduling parameter is updated online so that the tracking control performance is improved. Then, it is shown that the decision problem of the scheduling parameter and the controller state can be transformed into a convex optimization problem with respect to a scalar parameter. Based on this fact, we propose a numerically efficient algorithm for solving the optimization problem. Further, we propose a method of modifying the control algorithm so that the asymptotic tracking property is ensured even when the numerical error exists in the optimal solution. A numerical example and an experimental result are provided to illustrate effectiveness of the proposed control method.

scholarly journals Based on Robust Sliding Mode and Linear Active Disturbance Rejection Control for Attitude of Quadrotor Load UAV

2021 ◽  
Author(s):  
Zhaoji Wang ◽  
Tong Zhao
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Disturbance Rejection ◽  
Control Method ◽  
Sliding Mode ◽  
Reference Signal ◽  
Active Disturbance Rejection Control ◽  
Mass Variation ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

Abstract In this paper, a mass adaptive control method combining robust sliding mode control (SMC) and linear active disturbance rejection control (LADRC) is designed for the quadrotor load unmanned aerial vehicle (UAV) with mass variation. The scheme combines the advantages of SMC and LADRC. SMC can enhance the robustness of the controller, improve the anti-disturbance performance and overcome the problem of low control precision caused by bandwidth limitation of LADRC. The linear extended state observer (LESO) can estimate the external and internal disturbances of the system in real time, and then compensate the total disturbance through the PD controller. In order to simplify parameter setting, adaptive control is introduced in LADRC to adjust controller parameters in real time. In addition, adaptive law is also used to control the mass variation of the quadrotor. Then the stability of the whole system is verified by Lyapunov stability theory. Finally, the comparison with LADRC shows the superiority of the designed scheme, which can track the reference signal stably and effectively.

scholarly journals Prescribed Performance Control for the Upper-Limb Exoskeleton System in Passive Rehabilitation Training Tasks

2021 ◽  
Vol 11 (21) ◽  
pp. 10174
Author(s):  
Zhirui Zhao ◽  
Jichun Xiao ◽  
Hongyun Jia ◽  
Hang Zhang ◽  
Lina Hao
Keyword(s):  
Upper Limb ◽  
Control Method ◽  
Controller Design ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Sliding Mode Control ◽  
Model Parameters ◽  
Prescribed Performance ◽  
Upper Limb Exoskeleton ◽  
Model Free

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.

Fuzzy Takagi-Sugeno Method in Microcontroller Based Water Tank System

2018 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Nur Maisarah Mohd Sobran ◽  
Munirah Mohd Salmi ◽  
Mohd Bazli Bahar ◽  
Md Nazri Othman ◽  
Siti Halma Johari
Keyword(s):  
Fuzzy Logic ◽  
Mathematical Modelling ◽  
Real Time ◽  
System Performance ◽  
Fuzzy Logic Controller ◽  
Experimental Setup ◽  
Water Tank ◽  
Tank System ◽  
Initial Idea ◽  
Takagi Sugeno

This paper present the performance of Fuzzy logic controller in maintain level of water in water tank system. The mathematical modelling was developed to get the initial idea of the system performance. Later, the prototype of water tank system were constructed and tested to get the real time results. The Takagi-Sugeno “on” and “off” interference technique method was implemented due to the control limitation of the pump motor that being used in the experimental setup. The fuzzy logic controller was realized by embedded the algorithm in microcontroller of the water tank system. The experimental results show acceptable level of water within the range of 18cm to 20.5cm and settling time 59 seconds with 20 cm set point.

scholarly journals Fuzzy Logic Unmanned Air Vehicle Motion Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Chelsea Sabo ◽  
Kelly Cohen
Keyword(s):  
Fuzzy Logic ◽  
Motion Planning ◽  
Real Time ◽  
Failure Rate ◽  
Fuzzy Logic Controller ◽  
Target Location ◽  
Control Method ◽  
Optimal Method ◽  
Control Effort ◽  
Inference System

There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV) being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range) and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC) feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF) solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.

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