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.

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.

Neural Network Based Tracking Control of Mechanical Systems

1999 ◽  
Vol 121 (1) ◽  
pp. 148-154
Author(s):  
T. Efrati ◽  
H. Flashner
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Optimization Problem ◽  
Simple Structure ◽  
Tracking Error ◽  
Mechanical Systems ◽  
Closed Loop System ◽  
Control Effort ◽  
Learning Procedure ◽  
On Line

A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.

Robust Computed Torque Schemes for Mechanical Manipulators: Nonadaptive Versus Adaptive

1991 ◽  
Vol 113 (2) ◽  
pp. 324-327 ◽  
Author(s):  
Y. H. Chen
Keyword(s):  
Finite Time ◽  
Tracking Control ◽  
Tracking Error ◽  
Statistical Information ◽  
Torque Control ◽  
Control Algorithms ◽  
Computed Torque Control ◽  
Control Schemes ◽  
Mechanical Manipulators ◽  
Computed Torque

We consider the tracking control problem of mechanical manipulators in the presence of uncertainty. Two classes of control algorithms are proposed. If the possible bound of the uncertainty is known, a class of nonadaptive robust computed torque control schemes is used. The control guarantees the tracking error to be confined within a specified region after a finite time. If the bound of uncertainty is unknown, a class of adaptive robust computed torque control schemes is used. The control guarantees the tracking error to converge to zero. Both classes of controls are continuous. No statistical information on the uncertainty is ever assumed.

scholarly journals Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Machines ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 25 ◽  
Author(s):  
Raul-Cristian Roman ◽  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Emil Petriu
Keyword(s):  
Control Algorithms ◽  
Virtual Reference ◽  
Servo Systems ◽  
Model Free ◽  
Model Free Control

scholarly journals Model-free control algorithms for micro air vehicles with transitioning flight capabilities

2020 ◽  
Vol 12 ◽  
pp. 175682932091426 ◽  
Author(s):  
Jacson MO Barth ◽  
Jean-Philippe Condomines ◽  
Murat Bronz ◽  
Jean-Marc Moschetta ◽  
Cédric Join ◽  
...  
Keyword(s):  
Micro Air Vehicles ◽  
Control Algorithms ◽  
Model Free ◽  
Model Free Control ◽  
Air Vehicles

Path tracking control of electromechanical micro-positioner by considering control effort of the system

2020 ◽  
pp. 095965182095327
Author(s):  
Mohammad Reza Gharib ◽  
Ali Koochi ◽  
Mojtaba Ghorbani
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Path Tracking ◽  
Lumped Parameter Model ◽  
Proportional Integral Derivative ◽  
Control Effort ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Micro Actuators ◽  
And Control

Position controlling with less overshoot and control effort is a fundamental issue in the design and application of micro-actuators such as micro-positioner. Also, tracking a considered path is very crucial for some particular applications of micro-actuators such as surgeon robots. Herein, a proportional–integral–derivative controller is designed using a feedback linearization technique for path tracking control of a cantilever electromechanical micro-positioner. The micro-positioner is simulated based on a 1-degree-of-freedom lumped-parameter model. Three different paths are considered, and the capability of the designed controller on the path tracking with lower error and control effort is investigated. The obtained results demonstrate the efficiency of the designed proportional–integral–derivative controller not only for reducing the tracking error but also for decreasing the control effort.

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.

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 Adaptive Fuzzy Tracking Control for a Permanent Magnet Synchronous Motor via Backstepping Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Jinpeng Yu ◽  
Junwei Gao ◽  
Yumei Ma ◽  
Haisheng Yu
Keyword(s):  
Permanent Magnet ◽  
Tracking Control ◽  
Reference Model ◽  
Permanent Magnet Synchronous Motor ◽  
Tracking Error ◽  
Reference Signal ◽  
Small Neighborhood ◽  
Parameter Uncertainties ◽  
Fuzzy Logic Systems ◽  
Load Torque

The speed tracking control problem of permanent magnet synchronous motors with parameter uncertainties and load torque disturbance is addressed. Fuzzy logic systems are used to approximate nonlinearities, and an adaptive backstepping technique is employed to construct controllers. The proposed controller guarantees the tracking error convergence to a small neighborhood of the origin and achieves the good tracking performance. Simulation results clearly show that the proposed control scheme can track the position reference signal generated by a reference model successfully under parameter uncertainties and load torque disturbance without singularity and overparameterization.

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