Parameter Identification and Control Scheme for Monitoring Automatic Thickness Control System with Measurement Delay

Thickness accuracy is the important quality index of the hot rolling strip. Therefore, the automatic gauge control (AGC) is always the important function that the control system of hot rolling trip completes firstly. Due to the complicated rolling process, nonlinearity and time-variation of the controlled system, the simple AGC system can't obtain desirable effect. In the paper, a new single neuron-Smith prediction control system of AGC is presented. Stability analysis and the simulated experiments on this system are carried out. The simulated results indicate that the new system has the advantages of quick response, low overshooting, small steady-state error and good anti-interference ability. New control system improves the control performance of system with time delay and the accuracy of strip thickness.

The dead-zone input and hydraulic cylinder friction of the pump-controlled automatic gauge control (AGC) system introduce significant challenges to the high-precision rolling of lithium battery pole pieces. To address these nonlinearities, this paper establishes the friction and dead-zone model of the pump-controlled AGC system, and a slide-mode observer is designed to estimate the friction state z in the LuGre model. Furthermore, an adaptive compensation method is adopted to identify the unknown parameters of the input dead-zone and friction models. Meanwhile, combined with the framework of backstepping control design, both matched and mismatched disturbances are effectively compensated. Stability analysis guarantees the convergence of the estimation errors and closed-loop signal boundedness. Finally, experimental results validate the effectiveness and robustness of the proposed control strategy.

Abstract-Because the request to the quality of cold rolling sheet strips is higher and higher, the hydraulic AGC (Automatic Gauge Control) has been a requisite means for improving the quality of cold rolling strips' finished products. But the PID control strategy, being comprehensively used in the locale, is difficult to satisfy the request of improving the control precision of cold rolling mill, because robustness is not very strong. The dynamic matrix control, brought forward by Cutler, is one of predictive control theories. It adopts the non-parameter model based the step response of the controlled object, and adopts strategies of multi-step prediction, receding-horizon optimization and feedback correction, which has the good tracking characteristic. The dynamic matrix control has succeeded to be applied in the industry field, so it is indispensable and feasible to explore its application in the hydraulic AGC system of the cold rolling mill. But the step response of the whole hydraulic AGC can't be gained easily in the practical project, so it restricts the application of the dynamic matrix control in the hydraulic AGC. In this article, using for reference the thought of the cascade control, the predictive control is combined in the cascade structure. In the thickness loop of the hydraulic AGC, it adopts PID control. And in the inner loop of the screw-down servo system, it adopts the dynamic matrix control. The cascade predictive control in this article solves the problem of gaining the step response of the whole hydraulic AGC difficultly. When adopting the dynamic matrix control, the state observer is constructed, which makes the control system have the state feedback form with the observer, strengthens robustness of the system by correcting the predictive model, and makes the system have the intelligence. It synthesizes merits of PID control, which possesses of the easy algorithm and adjusting the parameter conveniently, the characteristic of the dynamic matrix control including of good tracking characteristic and strong robustness. The result of the experiment shows that the adopted control strategy makes the dynamic quality of the system improved largely. The response time and the control precision of the screw-down servo system are improved. And the thickness precision of the strip is improved too.

To meet the high thickness accuracy requirements in cold-rolling processes, a roll eccentricity signal extraction method based on modified particle swarm optimization and wavelet threshold denoising (MPSO-WTD) with intrinsic time-scale decomposition (ITD) is proposed. The strong denoising ability of the wavelet is combined with the decomposition and recognition attributes of ITD for non-stationary signals. Periodic disturbances in strip thickness caused by roll eccentricity are actively compensated. First, the wavelet is used to denoise the signal and the MPSO algorithm is applied to determine a rational threshold and improve the calculation efficiency. Then, the denoised signal is decomposed into proper rotational components (PRCs) using the ITD method, and an appropriate PRC component representing the eccentricity signal is extracted. Finally, the eccentricity compensation signal is applied in the automatic gauge control (AGC) system of the cold rolling mill. During the rolling process, the rolling speed is not constant and will directly affect the frequency of the roll eccentricity signal. To solve this problem, an encoder is installed at the end of the roll and the compensation frequency of the roller eccentricity signal is determined in the roller eccentricity compensation system according to the pulse number output. The results of simulations and experiments show that roll eccentricity signals extracted using the proposed method can effectively remove the influence of interference signals. An average improvement of 62.3% in the roll eccentricity compensation effect was achieved under the stable rolling condition in the finishing rolling stage.

To reduce longitudinal gauge deviations of the head sections at the hot strip mill rolling it is reasonable to profile band of variable thickness due to the length provided by the linear motion of the screw-down arrangements under load. The paper considers the structure of the automatic gauge control system (ACGS) of the indirect type. It estimates the accuracy of screw-down arrangement position control in the single-loop automatic position control system. It also justifies the use of automatic position combined control system. The introduction of the additional channel of control due to the first derivative of the reference input signal was proved to provide the required accuracy values of the strip gauge control. The paper presents data of the pilot study of the developed system at OJSC Magnitogorsk Iron and Steel Works' mill 2,000; they confirm its efficiency.

In order to fully understand dynamic behavior of the thickness variation and conduct off-line process tuning, a dynamic simulator for the feedforward Automatic Gauge Control (AGC) for a hot strip finishing mill in China Steel Corporation is developed. One of the major factors influencing on deviation of delivery thickness of the steel strip is the temperature variation caused by cyclic contact between the steel strip and beams, which transfer the strip in the hot rolling process. This temperature difference brings about hardness variation along the strip, which is known as skid marks. Simulation results and system analysis verify that the feedforward AGC system is capable of overcoming the high frequency thickness variation disturbed by the cyclic temperature difference, which usually cannot be effectively compensated by regular gaugemeter AGC feedback loop. A strategy to locate the optimal feedforward gain is also proposed.

This paper investigates the fuzzy control method for the linear switched reluctance motor (LSRM) drive system. Modeling of the LSRM is constructed based on the finite element method (FEM) environment Maxwell and the control system is analyzed by the co-simulation environment with Simplorer. Both the performance of the fuzzy proportional integral differential (PID) controller and the traditional PID controller are compared. The simulation results prove that the fuzzy PID controller yields faster rising time and smaller overshoot, compared with the traditional PID controller.

This work deals with the simulation model of multi-machines system as cold rolling mill is considered as application. Drivers of rolling system are a set of DC motors, which have extend applications in factories as aluminum rolling. Interconnection of multi DC motors in such a way that they are synchronized in their rotational speed. In cold rolling, the accuracy of the strip exit thickness is a very important factors. To realize accuracy in the strip exit thickness, Automatic Gauge Control system is used. In this paper MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results were presented to verify proposed model of cold rolling mill

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.

In strip rolling, hydraulic automatic gauge control (HAGC) system is the key element to guarantee the precision of strip gauge. The stability of the kernel pressure closed loop (PCL) in the HAGC system plays an essential role in guaranteeing the rolling process with high performance. Nevertheless, there is some difficulty in exploring the instability mechanism of the HAGC system due to the fact that the PCL is a representative nonlinear closed‐loop control system. In this work, for each component of the HAGC system, the mathematical model was established. And on the basis of the linking relation of various elements, we derived the incremental transfer model of the PCL system. Furthermore, in accordance with the deduced information transfer relation, the transfer block diagram of disturbing variable of the PCL system was obtained. Moreover, for the purpose of deriving the instability condition of the PCL system, the Popov frequency criterion was employed. The instability conditions of the HAGC system were obtained under PCL control. Furthermore, the derived instability conditions of the HAGC system were experimentally verified under various working conditions. The research results provide a fundamental foundation for studying the instability mechanism of the HAGC system.

Strip rolling is a very complicated nonlinear process, and characterized by couple between automatic shape control (ASC) and automatic gauge control (AGC), the coupling relationship between ASC and AGC heavily affects the improvement of combined controlling quality. This paper presents a kind of multivariable combination control system based on CMAC-PID. The perfect simulation results indicate that the ASC-AGC control system has good performances in adaptively tracking target and excellent anti-interference function. At the same time, the cross coupling between ASC and AGC is reduced.

Objective To design the control system of a magnetic resonance image (MRI) navigated pneumatic surgical robot. Method The driven system of the robot was constructed by combining with pneumatically driven and ultrasonic motor. The cylinder position control loop was established based on proportion integration differentiation (PID) control. The mathematical model of the pneumatic servo system was established by system identification. The PID parameter tuning and the simulation of the system characteristics were conducted. The stability analysis was conducted using the Nyquist criterion, and the acupuncture accuracy and the magnetic compatibility of the robot were verified. Results The simulation results showed that the maximum steady-state error was about 0.4 mm and the maximum tracking error was less than 2 mm, indicating that the PID control system had high control precision and trajectory tracking ability. The error of the acupuncture accuracy test was 0.72 mm. The results of water-modeling experiments showed that the magnetic resonance images were clear indicating that the influence of the robot on the MRI images was small. Conclusions The results of imulation, acupuncture and water-modeling tests showed that the pneumatic robot system can meet the requirements of brachytherapy surgery. Key words: Magnetic resonance imaging; Surgical robot; Pneumatic control system; PID control

This paper investigates the fuzzy Proportion Integration Differentiation (PID) controller for uncertain networked control systems (UNCSs). By combining T-S fuzzy model with the fuzzy PID controller, the uncertain networked control systems under transmission delays and external disturbances are proposed in this letter. Moreover, the asymptotically stability condition about the uncertain networked control systems is presented in the form of Lyapunov function and linear matrix inequality. The applicability of the proposed PID controller gain for uncertain networked control system is numerically tasted for various system parameters.

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

The automatic flatness control and automatic gauge control (AFC-AGC) is a complex system with strong nonlinear coupling and large time delay. With the requirement of further enhancement of product quality, putting forward decoupling control of strip shape and thickness is urgent. So in this paper, the decoupling control based on adaptability of the Radical is Basis Function (RBF) neural network, together with an on-line learning algorithm based on process optimum are proposed with good performances of decoupling and robustness.