A New Model-Free Stability-Based Cognitive Control Method

2014 ◽  
Author(s):  
Xi Nowak ◽  
Dirk Söffker
Keyword(s):  
Control Method ◽  
Mimo System ◽  
Equilibrium Points ◽  
Optimization Method ◽  
Control Input ◽  
Adaptive Stabilization ◽  
Whole Process ◽  
Model Free ◽  
Stabilization Control ◽  
And Performance

This contribution considers a new realization of the cognitive stabilizer, which is an adaptive stabilization control method based on a cognition-based framework. It is assumed, that the model of the system to be controlled is unknown. Only the knowledge about the system inputs, outputs, and equilibrium points are the preliminaries assumed within this approach. A new improved realization of the cognitive stabilizer is designed in this contribution using 1) a neural network estimating suitable inputs according to the desired outputs, 2) Lyapunov stability criterion according to a certain Lyapunov function, and 3) an optimization method to determine the desired system outputs with respect to the system energy. The proposed cognitive stabilizer is able to stabilize an unknown nonlinear MIMO system at arbitrary equilibrium point of it. Suitable control input can be designed automatically to guarantee the stability of motion of the system during the whole process although the changing of the system behavior or the environment. Numerical examples are shown to demonstrate the successful application and performance of this method.

Adaptive Neural Network Based Predictive Control of Nonlinear Systems With Slow Dynamics

2020 ◽  
Author(s):  
Mark Spiller ◽  
Fateme Bakhshande ◽  
Dirk Söffker
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Predictive Control ◽  
Mimo System ◽  
Prediction Equation ◽  
Control Input ◽  
System Behavior ◽  
Slow Dynamics ◽  
Model Free ◽  
Data Driven Approach

Abstract In this paper a data-driven approach for model-free control of nonlinear systems with slow dynamics is proposed. The system behavior is described using a local model respectively a neural network. The network is updated online based on a Kalman filter. By predicting the system behavior two control approaches are discussed. One is obtained by calculating a control input from the one step ahead prediction equation using least squares, the other is obtained by solving a standard linear model predictive control problem. The approaches are tested on a constrained nonlinear MIMO system with slow dynamics.

Control method for ventilated supercavitating vehicle considering planing avoidance and stability

2018 ◽  
Vol 233 (3) ◽  
pp. 957-968
Author(s):  
Seonhong Kim ◽  
Nakwan Kim
Keyword(s):  
Control Method ◽  
Ventilation System ◽  
Stability Loss ◽  
Vehicle Stability ◽  
Control Input ◽  
Supercavitating Vehicle ◽  
Additional Control ◽  
Avoidance Control ◽  
And Performance ◽  
Stability Issues

In this study, we focus on realizing planing avoidance control for a ventilated supercavitating vehicle while considering stability issues. A ventilated supercavitating vehicle can control the size of a cavity by blowing gas into the cavity. By controlling the cavity size, planing can be prevented in advance. However, the vehicle loses its stability because of the growth of the cavity. Additional control input is determined to prevent “stability loss” based on linear stability analysis while considering ventilation system dynamics. The proposed controllers are applied to a supercavitating vehicle model, and numerical simulations are performed to analyze the physical feasibility and performance of the designed controllers. The results show that the proposed control methods can maintain vehicle stability during maneuvering operations and can eliminate planing.

scholarly journals A New Model-Free Predictive Control Method Using Input and Output Data

2014 ◽  
Vol 1042 ◽  
pp. 182-187 ◽  
Author(s):  
Shigeru Yamamoto
Keyword(s):  
Mathematical Model ◽  
Predictive Control ◽  
Control Method ◽  
Just In Time ◽  
Control Input ◽  
Input Output ◽  
Output Data ◽  
Online Data ◽  
Model Free ◽  
The Mathematical Model

The purpose of this paper is to present a new predictive control utilizing online data and stored data of input/output of the controlled system. The conventional predictive control methods utilize the mathematical model of the control system to predict an optimal future input to control the system. The model is usually obtained by a standard system identification method from the measured input/output data. The proposed method does not require the mathematical model to predict the optimal future control input to achieve the desired output. This control strategy, called just-in-time, was originally proposed by Inoue and Yamamoto in 2004. In this paper, we proposed a simplified version of the original just-in-time predictive control method.

Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane

2019 ◽  
Author(s):  
Hoang Anh Pham ◽  
Dirk Söffker
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Structural Information ◽  
Control Strategies ◽  
Control Input ◽  
Tracking Errors ◽  
Adaptive Controllers ◽  
Control Approach ◽  
Model Free ◽  
Controlled Systems

Abstract Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.

Towards an Optimal Learning for Robust Iterative-Based Intelligent PID

2016 ◽  
Author(s):  
Elmira Madadi ◽  
Dirk Söffker
Keyword(s):  
Iterative Learning Control ◽  
Control Method ◽  
Sliding Mode ◽  
Learning Control ◽  
Iterative Learning ◽  
State Function ◽  
Dynamical Structure ◽  
Model Free ◽  
Intelligent Pid ◽  
And Performance

This contribution considers a model-free control method based on an optimal iterative learning control framework to design a suitable controller. Using this framework, the controller requires neither the information about the systems dynamical structure nor the knowledge about system physical behaviors. The task is solved using only the system outputs and inputs, which are assumed as measurable. The structure of the proposed method consists of three parts. The first part is implemented through an intelligent PID controller on the system. In the second part, a robust second order differentiator via sliding mode is applied in order to estimate accurately the evolution of the state function. In the third part, an optimal iterative learning control is chosen to improve the performance. Numerical examples are shown to demonstrate the successful application and performance of the method.

scholarly journals A 4-DOF Upper Limb Exoskeleton for Physical Assistance: Design, Modeling, Control and Performance Evaluation

2021 ◽  
Vol 11 (13) ◽  
pp. 5865
Author(s):  
Muhammad Ahsan Gull ◽  
Mikkel Thoegersen ◽  
Stefan Hein Bengtson ◽  
Mostafa Mohammadi ◽  
Lotte N. S. Andreasen Struijk ◽  
...  
Keyword(s):  
Upper Limb ◽  
Tracking Control ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Mechanical Design ◽  
Trajectory Tracking Control ◽  
Upper Limb Exoskeleton ◽  
And Performance ◽  
Physically Disabled People ◽  
Human Upper Limb

Wheelchair mounted upper limb exoskeletons offer an alternative way to support disabled individuals in their activities of daily living (ADL). Key challenges in exoskeleton technology include innovative mechanical design and implementation of a control method that can assure a safe and comfortable interaction between the human upper limb and exoskeleton. In this article, we present a mechanical design of a four degrees of freedom (DOF) wheelchair mounted upper limb exoskeleton. The design takes advantage of non-backdrivable mechanism that can hold the output position without energy consumption and provide assistance to the completely paralyzed users. Moreover, a PD-based trajectory tracking control is implemented to enhance the performance of human exoskeleton system for two different tasks. Preliminary results are provided to show the effectiveness and reliability of using the proposed design for physically disabled people.

High-Performance Image Filters via Sparse Approximations

2020 ◽  
Vol 3 (2) ◽  
pp. 1-19
Author(s):  
Kersten Schuster ◽  
Philip Trettner ◽  
Leif Kobbelt
Keyword(s):  
High Performance ◽  
Hardware Acceleration ◽  
Optimization Method ◽  
Translation Invariant ◽  
Approximation Quality ◽  
Trade Offs ◽  
Sparse Approximations ◽  
Image Filters ◽  
Good Trade ◽  
And Performance

We present a numerical optimization method to find highly efficient (sparse) approximations for convolutional image filters. Using a modified parallel tempering approach, we solve a constrained optimization that maximizes approximation quality while strictly staying within a user-prescribed performance budget. The results are multi-pass filters where each pass computes a weighted sum of bilinearly interpolated sparse image samples, exploiting hardware acceleration on the GPU. We systematically decompose the target filter into a series of sparse convolutions, trying to find good trade-offs between approximation quality and performance. Since our sparse filters are linear and translation-invariant, they do not exhibit the aliasing and temporal coherence issues that often appear in filters working on image pyramids. We show several applications, ranging from simple Gaussian or box blurs to the emulation of sophisticated Bokeh effects with user-provided masks. Our filters achieve high performance as well as high quality, often providing significant speed-up at acceptable quality even for separable filters. The optimized filters can be baked into shaders and used as a drop-in replacement for filtering tasks in image processing or rendering pipelines.

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