scholarly journals Design a Robust Sliding Mode Controller Based on the State and Parameter Estimation for the Nonlinear Epidemiological Model of Covid-19

2021 ◽  
Author(s):  
Ehsan Badfar ◽  
Effat Jalaeian Zaferani ◽  
Amirhossein Nikoofard
Keyword(s):  
Medical Treatment ◽  
Social Distance ◽  
Short Term Memory ◽  
Sliding Mode ◽  
The State ◽  
Sliding Mode Controller ◽  
Control Input ◽  
State Variables ◽  
Proposed Model

Abstract In this research, the vital problem of Covid-19 mitigation is looked at from an engineering point of view. At first, the behavior of coronavirus in society is expressed by a set of ordinary differential equations. In the proposed model, the control input signals are vaccination, social distance, and medical treatment. The unknown parameters of the system are estimated by Long Short-Term Memory (LSTM) algorithm. In the LSTM algorithm, the problem of long-term dependency is prevented. The uncertainty and measurement noise is an inherent characteristic of the epidemiological models. For this reason, an extended Kalman filter (EKF) is developed to estimate the state variables of the proposed model. In the rest of paper, a robust sliding mode controller is designed to control the spread of coronavirus under vaccination, social distance, and medical treatment. The stability of the closed-loop system is guaranteed by the Lyapunov theorem. The official confirmed data provided by the Iranian ministry of health authorities are employed to simulate the proposed algorithms. It is understood from simulation results that global vaccination has the potential to produce herd immunity in long-term. Under the proposed controller, daily Covid-19 infections and deaths become less than 500 and 10 people, respectively.

scholarly journals Robust Position Control of PMSM Using Fractional-Order Sliding Mode Controller

2012 ◽  
Vol 2012 ◽  
pp. 1-33 ◽  
Author(s):  
Jiacai Huang ◽  
Hongsheng Li ◽  
YangQuan Chen ◽  
Qinghong Xu
Keyword(s):  
Fractional Order ◽  
Position Control ◽  
Sliding Mode ◽  
Permanent Magnet Synchronous Motor ◽  
The State ◽  
Sliding Mode Controller ◽  
Sliding Surface ◽  
State Variables ◽  
Nonlinear Load ◽  
Special Case

A new robust fractional-order sliding mode controller (FOSMC) is proposed for the position control of a permanent magnet synchronous motor (PMSM). The sliding mode controller (SMC), which is insensitive to uncertainties and load disturbances, is studied widely in the application of PMSM drive. In the existing SMC method, the sliding surface is usually designed based on the integer-order integration or differentiation of the state variables, while in this proposed robust FOSMC algorithm, the sliding surface is designed based on the fractional-order calculus of the state variables. In fact, the conventional SMC method can be seen as a special case of the proposed FOSMC method. The performance and robustness of the proposed method are analyzed and tested for nonlinear load torque disturbances, and simulation results show that the proposed algorithm is more robust and effective than the conventional SMC method.

A novel robust finite-time tracking control of uncertain robotic manipulators with disturbances

2020 ◽  
pp. 107754632098244
Author(s):  
Hamid Razmjooei ◽  
Mohammad Hossein Shafiei ◽  
Elahe Abdi ◽  
Chenguang Yang
Keyword(s):  
Finite Time ◽  
Control Method ◽  
Sliding Mode ◽  
Robotic Manipulators ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
State Variables ◽  
Terminal Sliding Mode ◽  
Control Laws ◽  
Finite Time Boundedness

In this article, an innovative technique to design a robust finite-time state feedback controller for a class of uncertain robotic manipulators is proposed. This controller aims to converge the state variables of the system to a small bound around the origin in a finite time. The main innovation of this article is transforming the model of an uncertain robotic manipulator into a new time-varying form to achieve the finite-time boundedness criteria using asymptotic stability methods. First, based on prior knowledge about the upper bound of uncertainties and disturbances, an innovative finite-time sliding mode controller is designed. Then, the innovative finite-time sliding mode controller is developed for finite-time tracking of time-varying reference signals by the outputs of the system. Finally, the efficiency of the proposed control laws is illustrated for serial robotic manipulators with any number of links through numerical simulations, and it is compared with the nonsingular terminal sliding mode control method as one of the most powerful finite-time techniques.

Robust active disturbance attenuation control of an uncertain quadrotor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nigar Ahmed ◽  
Ajeet kumar Bhatia ◽  
Syed Awais Ali Shah
Keyword(s):  
Control Algorithm ◽  
Sliding Mode ◽  
The State ◽  
Disturbance Attenuation ◽  
State Variables ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Content Type ◽  
Highly Nonlinear ◽  
The Stability

PurposeThe aim of this research is to design a robust active disturbance attenuation control (RADAC) technique combined with an extended high gain observer (EHGO) and low pass filter (LPF).Design/methodology/approachFor designing a RADAC technique, the sliding mode control (SMC) method is used. Since the standard method of SMC exhibits a chattering phenomenon in the controller, a multilayer sliding mode surface is designed for avoiding the chattering. In addition, to attenuate the unwanted uncertainties and disturbances (UUDs), the techniques of EHGO and LPF are deployed. Besides acting as a patch for disturbance attenuation, the EHGO design estimates the state variables. To investigate the stability and effectiveness of the designed control algorithm, the stability analysis followed by the simulation study is presented.FindingsThe major findings include the design of a chattering-free RADAC controller based on the multilayer sliding mode surface. Furthermore, a criterion of integrating the LPF scheme within the EHGO scheme is also developed to attenuate matched and mismatched UUDs.Practical implicationsIn practice, the quadrotor flight is opposed by different kinds of the UUDs. And, the model of the quadrotor is a highly nonlinear underactuated model. Thus, the dynamics of the quadrotor model become more complex and uncertain due to the additional UUDs. Hence, it is necessary to design a robust disturbance attenuation technique with the ability to estimate the state variables and attenuate the UUDs and also achieve the desired control objectives.Originality/valueDesigning control methods to attenuate the disturbances while assuming that the state variables are known is a common practice. However, investigating the uncertain plants with unknown states along with the disturbances is rarely taken in consideration for the control design. Hence, this paper presents a control algorithm to address the issues of the UUDs as well as investigate a criterion to reduce the chattering incurred in the controller due to the standard SMC algorithm.

Adaptive output-feedback robust active disturbance rejection control for uncertain quadrotor with unknown disturbances

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nigar Ahmed ◽  
Syed Awais Ali Shah
Keyword(s):  
Output Feedback ◽  
Disturbance Rejection ◽  
Sliding Mode ◽  
The State ◽  
Active Disturbance Rejection Control ◽  
State Variables ◽  
Content Type ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Attitude Model

PurposeIn this research paper, an adaptive output-feedback robust active disturbance rejection control (RADRC) is designed for the multiple input multiple output (MIMO) quadrotor attitude model subject to unwanted uncertainties and disturbances (UUDs).Design/methodology/approachIn order to achieve the desired control objectives in the presence of UUDs, the low pass filter (LPF) and extended high gain observer (EHGO) methods are used for the estimation of matched and mismatched UUDs, respectively. Furthermore, for solving the chattering incurred in the standard sliding mode control (SMC), a multilayer sliding mode surface is constructed. For formulating the adaptive output-feedback RADRC algorithm, the EHGO, LPF and SMC schemes are combined using the separation principle.FindingsThe findings of this research work include the design of an adaptive output-feedback RADRC with the ability to negate the UUDs as well as estimate the unknown states of the quadrotor attitude model. In addition, the chattering problem is addressed by designing a modified SMC scheme based on the multilayer sliding mode surface obtained by utilizing the estimated state variables. This sliding mode surface is also used to obtain the adaptive criteria for the switching design gain parameters involved in the SMC. Moreover, the requirement of high design gain parameters in the EHGO is solved by combining it with the LPF.Originality/valueDesigning the flight control techniques while assuming that the state variables are available is a common practice. In addition, to obtain robustness, the SMC technique is widely used. However, in practice, the state variables might not be available due to unknown parameters and uncertainties, as well as the chattering due to SMC reduces the performances of the actuators. Hence, in this paper, an adaptive output-feedback RADRC technique is designed to solve the problems of UUDs and chattering.

scholarly journals Bus Voltage Control of DC Distribution Network Based on Sliding Mode Active Disturbance Rejection Control Strategy

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1358 ◽  
Author(s):  
Boning Wu ◽  
Xuesong Zhou ◽  
Youjie Ma
Keyword(s):  
Disturbance Rejection ◽  
Sliding Mode ◽  
Distribution Network ◽  
Sliding Mode Controller ◽  
Active Disturbance Rejection Control ◽  
State Variables ◽  
Active Disturbance Rejection ◽  
Dc Distribution ◽  
Disturbance Rejection Control ◽  
Bus Voltage

The DC distribution network has more advantages in power transmission, grid connection of distributed energy, and reliability of power supply when compared with AC distribution network, but there are still many problems in the development of DC distribution network. DC bus voltage control is one of the hot issues in the research of DC distribution network. To solve this problem, in this paper, a new type of sliding mode active disturbance rejection control (SMADRC) controller for AC/DC converters is designed and applied to the voltage outer loop. The linear extended state observer (LESO) can observe the state variables and the total disturbance of the system. The SMADRC is composed of a sliding mode controller, LESO, and disturbance compensator, which can compensate the total disturbance observed by LESO properly. Therefore, it improves the dynamic. At the same time, it can also reduce the system jitter that is caused by sliding mode controller. The state variables that are observed by the LESO are used in the design of sliding mode controller, which greatly simplifies the design of sliding mode controller. Finally, the simulation results of Matlab/Simulink show that the controller has good start-up performance and strong robustness.

DESIGN AND ANALYSIS OF SUPER TWISTING SLIDING MODE CONTROL FOR MACHINE TOOLS

2016 ◽  
Vol 78 (10-3) ◽  
Author(s):  
Chiew Tsung Heng ◽  
Zamberi Jamaludin ◽  
Ahmad Yusairi Bani Hashim ◽  
Nur Aidawaty Rafan ◽  
Lokman Abdullah ◽  
...  
Keyword(s):  
Machine Tools ◽  
Sliding Mode ◽  
Tracking Error ◽  
Control Algorithms ◽  
Sliding Mode Controller ◽  
Control Input ◽  
Positioning System ◽  
Direct Drive ◽  
Chattering Effect

High demands of precision on machine tools are hardly cope by using existing classic control algorithms. This paper focuses on the design, analysis and validation of a super twisting sliding mode controller on a single axis direct drive positioning system for improved tracking performances. The second order positioning system parameters were determined using input and output of measured data. Effects of two gain parameters in control algorithm on the quality of the control input and tracking error were analysed experimentally. The gain parameters were selected based on magnitude reduction in chattering during practical application. The performance of tuned super twisting sliding mode controller was compared with a traditional sliding mode controller using sigmoid-like function. Results showed that super twisting sliding mode controller reduced the chattering effect and improved the performance of system in terms of tracking error by 16.5%.  

scholarly journals Learning Robot Speech Models to Predict Speech Acts in HRI

2016 ◽  
Vol 9 (1) ◽  
pp. 295-306
Author(s):  
Ankuj Arora ◽  
Humbert Fiorino ◽  
Damien Pellier ◽  
Sylvie Pesty
Keyword(s):  
Speech Acts ◽  
Short Term Memory ◽  
The State ◽  
Short Term ◽  
Long Run ◽  
Action Sequences ◽  
Long Short Term Memory ◽  
Max Sat ◽  
Speech Model

Abstract In order to be acceptable and able to “camouflage” into their physio-social context in the long run, robots need to be not just functional, but autonomously psycho-affective as well. This motivates a long term necessity of introducing behavioral autonomy in robots, so they can autonomously communicate with humans without the need of “wizard” intervention. This paper proposes a technique to learn robot speech models from human-robot dialog exchanges. It views the entire exchange in the Automated Planning (AP) paradigm, representing the dialog sequences (speech acts) in the form of action sequences that modify the state of the world upon execution, gradually propelling the state to a desired goal. We then exploit intra-action and inter-action dependencies, encoding them in the form of constraints. We attempt to satisfy these constraints using aweighted maximum satisfiability model known as MAX-SAT, and convert the solution into a speech model. This model could have many uses, such as planning of fresh dialogs. In this study, the learnt model is used to predict speech acts in the dialog sequences using the sequence labeling (predicting future acts based on previously seen ones) capabilities of the LSTM (Long Short Term Memory) class of recurrent neural networks. Encouraging empirical results demonstrate the utility of this learnt model and its long term potential to facilitate autonomous behavioral planning of robots, an aspect to be explored in future works.

scholarly journals Optimal New Sliding Mode Controller Combined with Modified Supertwisting Algorithm for a Perturbed Quadrotor UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yassine El Houm ◽  
Ahmed Abbou ◽  
Moussa Labbadi ◽  
Mohamed Cherkaoui
Keyword(s):  
Sliding Mode ◽  
External Disturbance ◽  
Lyapunov Theory ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
Sliding Surface ◽  
State Variables ◽  
Quadrotor Uav ◽  
The Stability ◽  
Quadrotor System

This paper deals with the design of a novel modified supertwisting fast nonlinear sliding mode controller (MSTFNSMC) to stabilize a quadrotor system under time-varying disturbances. The suggested control strategy is based on a modified supertwisting controller with a fast nonlinear sliding surface to improve the tracking performance. The paper suggests a simple optimization tool built-in MATLAB/Simulink to tune the proposed controller parameters. Fast convergence of state variables is established by using a nonlinear sliding surface for rotational and translational subsystems. The modified supertwisting controller is developed to suppress the effect of chattering, reject disturbances, and ensure robustness against external disturbance effect. The stability of the proposed controller (MSTFNSMC) is proved using the Lyapunov theory. The performance of the proposed MSTFNSMC approach is compared with the supertwisting sliding mode controller (STSMC) by numerical simulations to verify its effectiveness.

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

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