Robust Control - Science topic

Call for papers: 2024 4th International Conference on Computer, Internet of Things and Control Engineering (CITCE 2024) will be held on November 1-3, 2024 in Wuhan, China as a hybrid meeting.

大会官网(投稿网址）：https://ais.cn/u/IJfQVv

大会时间：2024年11月1-3日

大会地点：中国-武汉

收录类型：EI Compendex，Scopus

第四届计算机、物联网与控制工程国际学术会议（CITCE 2024）将于2024年11月1-3日在中国-武汉召开。CITCE 2024将围绕计算机、物联网与控制工程的最新研究领域，为来自国内外高等院校、科学研究所、企事业单位的专家、教授、学者、工程师等提供一个分享专业经验、扩大专业网络、展示研究成果的国际平台，以期推动该领域理论、技术在高校和企业的发展和应用，也为参会者建立业务或研究上的联系以及寻找未来事业上的全球合作伙伴。大会诚邀国内外高校、科研机构专家、学者，企业界人士及其他相关人员参会交流。

1. 计算机科学：演算法、图像处理、计算机视觉、机器学习、智能数据分析与数据挖掘、数学和计算机建模、人工智能、神经网络、系统安全、机器人与自动化、信息系统、高性能计算、网路通讯、人机交互、电脑建模等；

2. 物联网：人工智能技术与应用、CPS技术与智能信息系统、物联网环境中的多网资源共享、物联网技术体系架构 、物联网中的云计算与大数据、边缘智能与区块链、智慧城市、物联网可穿戴设备 、智能家居、物联网与传感器技术等；

3. 控制工程：系统和自动化、电气系统、过程控制、工业控制技术、计算机科学与工程、电子工程学、软件工程、控制技术、传感器网络、移动互联网、无线网络和系统、计算机控制系统、自适应和最优控制、智能控制、电气自动化、智能控制和智能系统、智能管理和决策、分布式控制、驱动电机和控制技术、动力电池管理和维护技术、微传感器和执行器、移动机器人等

**其它相关主题均可

会议投稿经过2-3位组委会专家严格审核后，最终所录用的论文将被ACM ICPS (ACM International Conference Proceeding Series)(ISBN:979-8-4007-1184-8) 出版论文集，并提交至ACM Digital library，EI Compendex, Scopus检索。目前该会议论文检索非常稳定。

1、作者参会：一篇录用文章允许一名作者免费参会；

2、参会类型

（1）口头汇报：10-15分钟的全英PPT演讲；

*开放给所有投稿作者与自费参会人员；针对论文或者论文里面的研究做一个10-15min的英文汇报，需要自备PPT，无模板要求，会前根据会议邮件通知进行提交，详情联系会议秘书。

（2）海报展示：自制电子版海报，会议安排展示；

*开放给所有投稿作者与自费参会人员；格式：全英-A1尺寸-竖版，需自制；制作后提交海报图片至会议邮箱IC_CITCE@163.com，主题及海报命名格式为：海报展示+姓名+订单号。

（3）仅参会：非投稿作者，现场听众参会。

*仅开放给自费参会人员

（4）投稿参会链接：https://ais.cn/u/IJfQVv

S. Hasan Saeed treated wrote on Automatic Control Systems (with MATLAB programs) and one of his books has Chapter 12 as Robust Control Systems. If you have the book, kindly take snap shot of chapter 12 only and send to jocianvef2004@gmail.com, please. I am dare in need of his explanation in that topic.

The topics of interest for submission include, but are not limited to:

1. Electrical Engineering

Embedded systems

Optimal, Robust Control

...

2. Control Science

Power system optimization

Power distribution automation systems

...

What kind of nonlinear strategies can be utilized as the best controller in the condition that there is no information about the model of the system?

Consider that the model of the nonlinear system is unavailable and there is no information about it, regardless of estimating the model, which means that the model is very complicated and we can not estimate it properly. In the mentioned situation, what kind of controller could have better performance in the same conditions?

For instance, robust controllers such as sliding mode controllers have good performance.

Dear Colleagues ; I am interested in studying adaptive control and i need to answers some questions they are as follows:

*Who which gives the good performance between a direct or indirect MRAC?

*Can i apply the MRAC if the system has disturbance ?

*Is MRAC robust control ?

*What is a relationship between the a parameter estimation of the system and MRAC?.

hello dear colleagues

I was working on a problem where I decide to merge sliding mode controller with homogeneous one. I was planning to define the sliding manifold based on homogeneous system of integrators.

Has anybody tried it out? are there any advantages?

best regards

I'm looking to detect senescence in Zebrafish embryo and I need a robust control positive (and negative).

Kishi Shuji suggest BHP but exists a better control?

In controller design for mixed H₂ - H_inf performance, ||W₁(s)S(s)||_inf<1 and ||W₂(s)T(s)||_inf<1 are to be satisfied simultaneously. The question is how to obtain the weight W₁(s)?

Is there any generalized way to obtain W₁(s) for any given system?

Is there a practical MATLAB implementation for controller synthesis?

There are various works robust performance and mixed H-2/H-inf robust control. What is the difference between them?

What is the elaboration of various control laws in comparison to each other. It means, in the level of comparison, when to take Adaptive control? when to adopt Robust control? when for Neuro-fuzzy control? when for Optimal control? when for model predictive control?

I am designing a robust deadbeat controller for a grid-connected inverter with a pre-filter. But after putting the inner and the prefilter controller, my overall system became a six order system. So want to learn from control experts, if that is possible. Also, what could be the effect of additional poles and zeros on the overall behaviour of the system. Your contribution will be highly appreciated.

Hi,

Does anyone have experience with red fluorescent reporters (particularly mKate2) under various promoters, in yeast? We are particularly trying to express mKate2 under not a strong promoter and do not observe any color (compared to a robust control pDRF1-GW mKate2-T2A-mTurquoise2).

We are seeing good mRNA levels (ct values comparable to ACT1 mRNA using qRT-PCR) but cannot observe any protein product. We have also sequenced and verified integrity of the ORF.

Has anyone had similar problems with mKate2 or other fluorescent proteins? What could be some reasons why the mRNA can't be translated?

More precisely, in order to evaluate the controller performances, the IAE (integral of the absolute error) (or ISE: integral of the squared error) is generally used. Moreover, as well known in the HOSM theory, such controller is characterized by its sliding accuracy attained after the establishment of a real sliding motion (which mainly depend to the sampling time in the absence of noise and to the noise magnitude otherwise). The question is as follows: how can we interpret and discuss the tracking performance results provided by such HOSM controller using the both IAE criteria and SM accuracy in the same time?

In other hand, if we have different HOSM controllers (two SM-2 controllers for example), which provide the same IAE approximately but acheive a different order of sliding accuracies, how can we discuss this case?

Hi,

What are the books and/or papaers/reports that are recommended in order to understand fullly the mathematical stuff in the following book:

Robust and Optimal Control by Kemin Zhou et al.

i.e. how the thorems are constructed and the assumptions are made and ultimately how they are proved. It will be helpful to understand the historical devlopment as well.

Thanks & regards

We can derive the robust control problem by matrix inequalities to LMIs or use Riccati based iterative algorithms.

What's the advantages of the Riccati iterative methods such Kleinman algorithm compared with LMI ?

I have designed a robust controller for the control of grid tied PV system. The control involves controlling the active and reactive current reference currents in dq frame. Now i want to see if the controller is robust against any disturbances. I have decided to use part of reference signal as a disturbance. However i am not sure where to add this disturbance? I am using the universal bridge as inverter unit in simulink. Any help would be highly appreciated. Thank you.

I want to initiate a discussion about the major differences between planning and control methods. This question is also related to the difference between feedforward and feedback, right?

Many computer scientists are developing power algorithms used for planning and navigation while on the other hand control theorists are also working on advanced robust control algorithms for achieving probably similar tasks. How can we integrate both and at which level?

I hope my question is relevant!

My objective is to track the time varying delay and at the same time design a robust control for the system under time varying delay and multiple delay under uncertain conditions.

How can I design a robust PID controller for a second order plant having parametric uncertainties using h infinity. I tried using 'hinfsyn' function in matlab but it gives a higher order controller. Is it possible to convert it to a PID structure.

I also tried using 'systune' command in matlab to tune PID block in Simulink, there how to decide on TuningGoal.Senstivity class parameters (i.e. maximum sensitivity to disturbance as a function of frequency).

Thank You.

I have designed a robust controller for a 3rd order uncertain system (having only poles) described by 5 tr functions. Each tr function corresponding to a particular range of input. I want to simulate the implementation on a practical system. Which system should I consider?

Hi,

Selecting the H∞ controller weighting parameters is one the most important part of designing of such a good controller, however, there is no specific method in the most references to do that and most of the works have been done by the trial and error methods.

Do you have any suggestion in order to tune the H∞ controller?

Thanks.

Although I know that analytic functions are infinitely differentiable and the taylor series converges to the function but I want to know why they are so useful from the perspective of robust control theory? Can a non-analytic function perform as well as an analytic function?

It is well known that every controller, when optimized in a proper way, will yield good results. Under such circumstances, how should I compare the relative performance of two different controllers that can help me ascertain the superiority of one controller over the other. How can I reduce the bias in tuning so as to genuinely claim the superiority of one over the other ?

Hi

Consider the following closed loop generalized plant. P is the generalized plant, K is the controller to be designed and $\Delta$ matrix is the uncertainty matrix with the norm of smaller than one for all combination of the parameters.

I try to get the controller with H_inf design but the H_inf norm of the resulting closed loop from w_u to z_u is larger than 1. What does it mean?

I have tried to assume very power full actuation ( limitless). It never get smaller than 1. From theory means the controller is not robust. but why it is not possible to make it robust.

P.S: the real plant has two poles on origin.

I am trying to develop a control strategy to optimize the control of output temperature in a heat exchanger, the measurements show a non-minimum phase response to step inputs (medium massflow).

I am trying to use the covariance R as means to reject this behaviour with the aid of a simple model (power balance), so when a big disturbance comes, the R value goes very high as "no believing" in the measurement, then feeding the resulting estimate to the PID controller.

Does this makes sense??

In integrated direct/indirect adaptive robust control method, a standard projection mapping in adaptive control is used, where the outward unit normal vector is used to calculate the mapping. How to obtain the outward unit normal vector? Is there corresponding material to explain it? I can not find the right explanation.

What I know about the definition of ''Robustness Property'' for a controller designed for a particular set of parameters of the system it can be said to be robust if it works well under high-gain feedback to reject the disturbance gained by the system and eliminate the effect the system parameter uncertainty. So, is there any relation between robustness of the controller and faster response time such as raise time maximum overshoot settling time etc..?

 Truly Yours

In linear system, to improve bandwidth, we can use 2 DOF control design with a reference filter. The design is performed in frequency domain. Is there an equivalent method in control of nonlinear system? Let say if I am using backstepping and Lyapunov theory to develop the control.

I have to design a controller using Convex Optimization and Sum of Squares technique, please suggest some papers on this. What are the advantages of using this techniques from the classical controllers or other robust controllers.

Robustness control for control systems with temporal and non-temporal constraints across temporal and interval Petri nets

Assuming that, the H-infinity norm of the transfer function H(s) is less than 1 + delta, where delta is a small number.

How to prove that open loop transfer function (at the input plant), L(s), will approximate the target loop shape in this H-Infinity setup shown on the figure attached. 

Definitions;

Wp(s) = the target loop shape

'r' and 'nw' = the exogenous input (H-infinity framework)

'ew' and 'y' = the outputs ((H-infinity framework)

H(s) = closed transfer function from exogenous input to the outputs.

I have to design a robust H_inf controller for an LTI system which has a known input  besides disturbances and control inputs. (this input is not disturbance or control input)

"{m}" is a known input vector (measurable), "{w}" is disturbance vector and "{u}" is a control input vector.

How can I design this controller in a MATLAB m file? ({m} is not supposed to be considered as a disturbance or control input) 

I need hardware model

Hi all,

I'm trying to stabilize the following system in finite time

\dot x ^ n = \varphi + \gamma * u 

u is the control, \varphi and \gamma>0 are bounded with UNKNOWN bound. (Standard SMC Problem)

My objective is to force the state x to predefined neighborhood of zero ( |x|<\epsilon ) in finite time T.

with \epsilon and T are predefined and independant on \varphi and \gamma and the initial condition of x.

did any one try to solve this problem. Or did some one prove that it is IMPOSSIBLE to have this objective. 

Regards

Hi,

I am looking to design a robust power oscillation damper for a large power system network. As you know, I will have to obtain the information like eigen values etc for which I could use a software . My question is their any software which allows to design my own robust controller and later on implement  with the test system and still obtain the crucial information like eigen values and time domain simulations?

Thank you in advance

When picking up an object at a distance from its center of mass, a torque is applied to the gripper, increasing the amount of force required to maintain grip security. I've found a lot of research on torsional friction for contact surfaces and torque applied to joints within an arm but surprisingly little for this.

What is currently being done to compensate for this additional force that is needed? 

Hi everyone,

I plan to modify some renewable energy converter structure (say PV and wind) and study their effect on low frequency oscillations in the grid. Once, I modify, I want to connect it to a grid or a large number of buses. My queries are:

1) I'm aware that most commercial and free software have their own built in models and can give information about eigen values. Is their some way so that I can similar information for my modified model?

2) How does our fellow authors test say a new robust controller that they built in a large IEEE bus system?  

Thank you in advance

Dear Friends, I am working on the following hammerstein model:

x(t)=1.5*u(t)-1.5*u(t)^2+0.5*u(t)^3;

y(t)=0.6*y(t-1)-0.1*y(t-2)+1.2*x(t-1)-0.1*x(t-2);

PID control law with time-variant PID parameters is employed:

u(t)=kp(t)*(y(t)-y(t-1)) + kd(t)*(y(t)-2*y(t-1)+y(t-2)) + ki(t)*e(t); But the control results for this System is not good!

the MATLAB code which utilizes is attached. 

The positive definiteness and boundedness of inertia matrix are related to the maximum and minimum singular values of inertia matrix. some of books are only considering the revolute joints for boundedness. so, clarify me why only revolute? why not prismatic? what if the combination of prismatic and revolute in a same chain? and Is this because of units matter (cm/m/mm and rad/deg)?

Hello everyone,

I am currently doing some research in multiple objects tracking using Image Based Visual Sservoing methods. I have already found some papers about the topic of IBVS in general and also about my main concers - keeping multiple objects in the field of view of the camera. Do You know some must-read papers? Maybe someone is also doing a research on similar topic ? I would not mind sharing ideas at all. 

I am also interested in methods of extracting the depth to the tracked object and in any promising ways of adaptation to uncertainy in this parameter. I am using a monocular vision.

Any help would be appreciated.

A closed loop speed control of a photo voltaic fed induction motor drive system was designed and controlled using a PID controller. The torque ripple which was around 4.5Nm was considerably reduced to 0.4Nm when the controller was replaced by a fuzzy logic controller. Is this value of torque ripple and hence the corresponding ripple current justifiably less for the controller to be replaced?

Extended State Observer

I designed a robust controller based on H_infinity methods with help of matlab, I try to implemented this controller in hardware like (arduino,raspberry pi,pcduino...),is there an example?

or some thing can help to do that?

Is it possible to use Sliding Mode controller for Single input multiple output system?

Please suggest any book or paper that can help me with this.

Thank you

As far as I know, conventional sliding mode control technique requires the upper bounds of uncertainties to prove the Lyapunov stability.

However, sometimes estimating these bounds is very hard or almost impossible in real-world systems. So I am wondering if there are some SMC approaches that do not require the bounds of uncertainties.

Thank you very much.

In the field of model order reduction, if all the poles lie at the same point for a higher order system, then to reduce the order of the system is very difficult. Can anyone tell me which method is suitable to reduce the system, especially in frequency domain methods?

I am trying to compare no controller, with pid controller and fuzzy pid controller in matlab?

What is the significance of full rank of the expression in the figure with reference to fault detection?

I want to know about why fuzzy is used for boiler controls instead of other controllers such as model predictive controllers or robust controllers using u-synthesis technique?

What does it mean normalizing a random signal in robust control? For example, normalizing a disturbance signal and passing it through a filter to feed the plant input.

Please provide comprehensive response.

Regards,

May I know the advantages of Sliding mode control compares to others robust controllers such as nonlinear PID etc?Could anyone provide with literatures that highlight about the advantages of sliding mode controller if applied to AUV? 

For calculating the smoothness of the controller

I have some dificulties to understand how to compute the parameter 'p' in the algorithm in order to determine the deflating subspace Q (algorithm 2 step 3).

Hi,

I developed a H-infinty controller, however I would like to extend with anti-windup. In  S. Skogestad and I. Postlethwaite: MULTIVARIABLE FEEDBACK CONTROL book,there is a short description about the Hanus form, I tried to implement it using Matlab/Simulink, but the controller get unstable. Can anybody provide me a simple Matlab/Simulink example for a MIMO system with anti-windup? 

Regards,

Robert

I want to design a feedforward controller using LMI. Can you recommend me a good method to design this type of controller using for example Hinf approche or publication on this subject?

Regards

In open loop control to output transfer function of dc to dc converter, on which factor does the order of zeros depend on? As in case of order of poles  which depends on the number of passive elements present in the converter circuit.

Sliding Mode Controller is a Robust Controller, So to reduce the chattering any Type of linear controller such as (PID) added to SMC, Is it robust?

I search for a comprehensive document about Adaptive Robust Control (ARC) and its method. Can anyone help me to introduce or provide me a document in this subject?

I have an uncertain plant. I need to describe it using a number of transfer functions by conducting an experiment. I want to use Hudzovic method of identification and modelling.

Terminal SMC and its variants are finite time controller based on sliding mode control. Is there any other robust and finite time controlller available?

I am working with a humanoid robotic arm. The joints have dc servo motor with position and velocity sensor. But the sensor data is delayed. I have come across several methods including delayed input, transmission delay in telerobotic system etc. But it will be very helpful if somebody could guide me to which type of method I should follow. 

I know it is very general question but it makes me confused, because I've read many papers most of those use PD sliding surface and some use PID surface, but there are some papers that use different types of sliding surface and may they used auxilary state. I want to know how can I formulate the state equation for auxilary state.

In many papers, we use homogeneous controller of negative degree, so that we proof the asymptotic stability to deduce the finite time stability.

Let us take, the pure double integrator system

\dot x = y; \dot y = u,

with u = -l*sign(y + |x|^0.5sign(x))

according to Levant and Emelyanov, for $l > 0.5$, our system is finite time stable. I agree.

Now, let us take $0<l<0.5$ (l=0.25 for ex.), we can prove that the closed loop system is asymptotically stable, but it seems that the convergence is only asymptotic, not finite time.

Am I right?

The switching model of dc/dc converter is averaged and linearized to obtain the small signal model (SSM). SSM is then used to design controllers such as PI, state-feedback-controller etc, which can achieve control objective after tuning. Now, when disturbance is introduced this controller seems to amplify the disturbances, such that open-loop plant performs better than closed-loop. I want to achieve disturbance cancellation by disturbance-feed-forward. So, my question is how to go about this as the available techniques for disturbance observers seems not to work because in order to achieve such, the system has to be minimum-phase, however, the PWM converter in question is non-minimum-phase. Any idea here?

How does iPID deal with input and output constrains (especially when there are strict upper and lower output bounds)? Does that affect its performance? And to what extent?

I want to implement a High GA in Observer for a non-mimnimum phase TORA system. Do I have to apply only extended HGO or will a simple HGO suffice?

Can anyone help me with the algorithm used in fitfrd (used to be fitsys in the previous MATLAB versions)? I know that the command is used to fit the frequency response data with a state space representation model. I want to know the algorithm that is used in these two commands. The MATLAB help document didn't help. By the way, this command (fitfrd) is in Robust Control Toolbox and the (fitsys) was available in mu-synthesis and analysis Toolbox.

Thanks.

I did some research in this area and I want to be aware of other researcher's favorites.

I have the following problem. Consider a system \dot x1 = \mu f1(x1,x2,x3), \dot x2=\mu f2(x1,x2,x3), \dot x3 = f3(x1,x2,x3), where 1 \le \mu \le M and f_i's are polynomial. If I find a Lyapunov function in the vertex \mu = 1 and a Lyapunov function in the vertex \mu = M, can I conclude that the system is asymptotically stable for all 1 \le \mu \le M?

The requirement of the controller to force the system output to follow the reference output model as closely as possible is the existence of G and H.

I designed a robust controller based on H_infinity methods in MATLAB. How to implement the designed robust controller in Real Time using Micro-controller or DSP? What are the difficulties for real time implementation? Are there any standard procedures?

I am using PXR5 TCY1-1V070 Fuji PID controller for heating my specimen for a controlled heating with ramp function. I am interested in heating the specimen (aluminium block) at a specific rate as fast as possible (ramp time 1 min). I want to heat the specimen from 25 Deg C to 43 Deg in one min. I did Autotunning (AT) at the fixed set point 43 Deg. Got some P, I and D values. I want to use ramp function for heating where I have set parameters as follows SV1 (target1)---25, TM1r (Ramp time)---0.01, TM1S (soak segment time)--0.03, SV2 (target2)---43, TM2r (Ramp time)---0.01, TM2S (soak segment time)--0.08. But significant overshooting is observed each time i.e. specimen gets heated to 50 Deg C although second target point is 43 Deg C. Along with this the target temperature is not achieved in given ramp time (1 min) it takes more time. There is lag between SV and PV where as SV follows ramp time properly but ideally PV should follow SV for successful completion of ramp soak function. I contacted engineer he asked me to double P value but its not working on the contrary the heating is taking more time than that is set .

I am using band type heater and J type thermocouple.

Kindly suggest the solution to avoid over heating. What parameter do I need to adjust so that the ramp soak function will work properly?

Suppose, you are able to get only the position information with noise. If the velocity is less vulnerable to noise, we can estimate the volocity and to noise filtering and convert to position if necessary.