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ScienceDirect Procedia Computer Science 103 (2017) 355 – 362
XIIth International Symposium «Intelligent Systems», INTELS’16, 5-7 October 2016, Moscow, Russia
On PI-controller parameters adjustment for rolling mill drive current loop using neural tuner Y. Eremenko, A. Glushchenko, V. Petrov* Stary Oskol Technological Institute n.a. A.A. Ugarov (branch) NUST "MIS&S", Stary Oskol, Russia
Abstract We consider a problem of an on-line tuning of linear controller parameters used for a rolling mill drive control. A neural tuner of PI-controller for current control loop is developed. It allows to adjust KP and KI according to armature winding parameters values change. Moreover, if initial values of controller parameters do not provide required transients quality, proposed system will tune them to solve this problem. A functional block diagram, a neural network structure, and a rule base are shown. Two sets of modelling experiments are conducted with a roll mill model. Obtained results show transients quality improvement in speed and current control loops. In addition to this, energy consumption of the control system with the tuner is lower by 1-2% comparing to conventional PI-controller basic system. © Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2017 2017The TheAuthors. Authors. Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the XIIth International Symposium «Intelligent Systems». Peer-review under responsibility of the scientific committee of the XIIth International Symposium “Intelligent Systems” Keywords: current control loop; direct current drive; neural tuner; PI-controller; rolling mill.
1. Introduction An energy requirement of a rolling process is one of the highest among all technological processes in the metallurgical branch of industry. The rolling consists of rolling mills of different types, and such sufficient energy consumption can be explained by the fact that high power electric drives are used to rotate rolls of such mills. Even few percent reduction of these drives energy consumption will bring down prime cost significantly. This is especially actual for roughing mills.
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1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the XIIth International Symposium “Intelligent Systems” doi:10.1016/j.procs.2017.01.121
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Automation plays a key role in this problem solving. The point is that well-known P- and PI-control algorithms are widely used in automatic control systems of the technological processes under consideration, as well as in 9095% of all control loops in industry as a whole1. PI-controllers popularity can be explained by their structure simplicity and ease of use from staff point of view. But, despite all the advantages, such controllers require a plant to be linear in order to keep demanded transient quality. This could be considered as a main disadvantage of such control algorithm, since a rolling mill is highly nonlinear, that is its parameters change during functioning. That is caused by the following reasons: nonlinear electric drive characteristics; equipment deprecation; rolling metal temperature, geometry, rolling speed are changed. Taking all these points into consideration, the conclusion could be made that if the task is to keep the transients quality in all rolling mill functioning modes at the same level, then PI-controllers with permanent KP and KI values usage are not enough. Especially if controller parameters have been calculated once at the end of a run-in period, and their further adjustment have not been made2. Such problem can be solved by rolling mill adaptive control system development, which is responsible for linear controllers parameters values on-line tuning. To develop such system existing methods of PI-controller parameters tuning are considered. Most of the known methods demand either an accurate plant model to be known 3,4, or a test signal usage to identify such model parameters5,6. This is rather a difficult task to obtain accurate nonlinear model of a rolling mill under the condition of a real production. At the same time, automatic process control system engineer is usually able to tune PI-controller parameters without a model. They use their experience and knowledge on the plant behaviour features. These abilities could be taken into consideration by intelligent methods usage. Such methods include particle swarm optimization 7,8, fuzzy logic9, genetic algorithms10,11, neural networks (NN)2,12, etc. As far as a rolling mill is considered, the main requirements to the adaptive control system for such plant include: 1) the need to train such system on-line in order to follow plant parameters values change, 2) the need to store knowledge about the plant features (for example, about limits on different measured signals values). The first demand could be complied with the help of neural networks, whereas the second one – with expert systems application. In this research these methods are proposed to be combined. In13 we suggested a PI-controller neural tuner. It consists of a neural network, which output neurons calculate KP and KI values, and a rule base determining when and with what learning rate such network is needed to be trained. This tuner has been used to control heating furnaces functioning in different modes. Such method seems to be perspective to be applied for tuning of controllers parameters values used in a rolling mill DC drive. A rule base to tune KP and KI values of a rolling mill armature current loop PI-controller is developed. A modelling experiment has been conducted using simplified two-high reversing rolling mill control system mathematical model. 2. Problem statement Let's consider two-high reversing mill with certain electric drive for each roll. Drive torques of a top and a bottom rolls are provided by two separately excited electric DC drives. Dual-zone control system is used for each drive (figure 1). A two-loop armature voltage control scheme is used in the first zone. A two-loop exciting current control scheme is used in the second zone. The main objective of this research is an armature current control loop in a mentioned above two-loop control scheme of the first zone. This loop plant is an armature winding. It can be modelled by a first order aperiodic link with gain coefficient K and time constant Te. These parameters change their values during drive functioning due to winding heating and wear. At the same time, this loop PI-controller parameters have been calculated once as a result of a start-up and commissioning operations. They have never been changed since that time. This eventually results in energy consumption rise. We propose to apply the neural tuner to adjust armature current PI-controller parameters on-line in accordance with the plant parameters change. A plant model is not needed.
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Fig. 1. A functional block diagram of a two-high reversing rolling mill electric drive: 1 – thyristor converter; 2 – motor armature; 3 – armature current controller; 4 – current sensor; 5 – speed controller; 6 – speed set-point adjuster, 7 – tachometric generator; 8 – electromotive force controller; 9 – rectified electromotive force set-point adjuster; 10 – exciting current control; 11 – exciting current sensor; 12 – drive winding; 13 – thyristor exciter.
3. Neural tuner structure description A neural tuner functional block diagram is shown in figure 2. It consists of: 1) a rule base determining situations, when the PI-controller should be tuned (such rules consequences contain learning rate values for certain neurons of a neural network output layer); 2) a neural network, which receives data about setpoint values for armature current and speed control loops, a control action from the tuned controller and a plant output value.
Fig. 2. A functional block diagram of the control system with the neural tuner.
Let's consider the neural network structure. It is similar to the one described in13 for heating plants control. It consists of three layers according to14. Its input layer consists of five neurons: armature current setpoint value, current output value delayed on dt seconds, Δt seconds and 2Δt seconds, and armature current PI-controller output. dt is a modelling step size, Δt – delay time of neural network input signals from each other. There are 15 neurons and sigmoid activation function in the hidden layer. The output layer includes two neurons, which are responsible for KP and KI values calculation, and a linear activation function. Δt value is calculated in accordance with the method proposed in13. Δt is equalled to 1.5 ms. The tuner is called once in Δt seconds. The neural network is trained on-line in order to follow armature current loop plant parameters values change. The backpropagation method is used to do that15. We propose to use different learning rate values for each neuron of the network output layer. It is caused by the fact that KP and KI values may differ by several orders of magnitude. All learning rates are equalled to nil at the beginning of each iteration of the neural tuner functioning. Then the rule base is called. As a result certain value of the learning rate for each neuron is determined. Then one iteration of the backpropagation method is made.
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4. Rule base The learning rate values and moments when the neural network should be trained are determined by the rule base. It makes qualitative evaluation of the armature control loop output signal change caused by this loop setpoint value calculated by the speed controller. Each transient is evaluated from the moment when the speed control loop setpoint value is changed. 4.1. Rules to determine the learning rate for the output layer neuron, which is responsible for KP If armature current curve crosses setpoint curve, and the first maximum of the setpoint curve has not been reached, then KP value is needed to be decreased, and the learning rate for the first neuron of the output layer is one order of magnitude lower in comparison with the current KP value. If armature current curve has become closer to the setpoint curve in comparison with the previous neural tuner call, and the first maximum of the setpoint curve has not been reached, then KP value is needed to be decreased, and the learning rate for the first neuron of the output layer is two orders of magnitude lower in comparison with the current KP value. If the absolute value of the second extreme point of the setpoint curve is higher than the absolute value of the second extreme point of the armature current, and KI has not been tuned during this transient, and armature current and setpoint curves have crossed at least once since the beginning of the transient, then KP value is needed to be increased, and the learning rate for the first neuron of the output layer is two orders of magnitude lower in comparison with the current KP value. If the absolute value of the first extreme point of the setpoint curve is more than two times higher than the absolute value of the first extreme point of the armature current, then KP value is needed to be increased, and the respective neuron learning rate is one order of magnitude lower than current KP. If armature current value curve has crossed the setpoint curve between the first and the second extreme points of the setpoint curve, then KP value is needed to be decreased, and the learning rate for the output layer first neuron is two orders of magnitude lower in comparison with the current KP value. 4.2. Rules to determine the learning rate for the output layer neuron, which is responsible for K I If the absolute value of the first extreme point of the armature current curve is higher than the absolute value of the first extreme point of the setpoint curve, and these curves have not crossed between the first and the second extreme points of the setpoint curve, and the neuron responsible for KP has not been trained since the beginning of the current transient, then KI value is needed to be decreased, and the learning rate for the first neuron of the output layer is three orders of magnitude lower in comparison with the current KI value. If there is steady-state error between real armature current and setpoint values after the end of the transient, and its value is higher than 10% of the current setpoint value, Then KI value is needed to be increased, and the learning rate for the first neuron of the output layer is two orders of magnitude lower in comparison with the current KI value. 5. Neural tuner implementation for current control loop A simplified mathematical model of the two-high reversing rolling mill control system of the rolling shop №1 of the Oskol Electrometallurgical plant is used to conduct experiments. This rolling mill is equipped with separately excited electric DC motors 1JW5539-5DK07-Z-001. The main aim of this research is to apply the neural tuner to adjust PI-controller used in the armature current control loop, so the exciting current control loop is not considered. Developed model is shown in figure 3.
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Fig. 3. Simplified model of the rolling mill DC drive.
Both speed P-controller and armature current PI-controller parameters were calculated according to the technical optimum requirements. For the armature current control: KP = 0.24, KI = 6.705. For the speed control: KP speed = 1.92. The setpoint schedule was implemented in a S-function setpoint as a following sequence of values: 0 rpm (0 V) → 60 rpm (10 V) → 0 rpm (0 V) → -60 rpm (-10 V). The model of the armature winding was implemented as a first order aperiodic link within a S-function aperiod. It allowed to change this link parameters K (nominal value – 41.67) and Te (nominal value – 0.036 s) during the modelling process. Sat and Sat1 blocks were used to limit speed and current controllers output signals to the interval [-10 V; 10V]. A windup protection is implemented in the PI-controller. If (and while) its output is higher than 10V or lower than -10 V, then KI is equaled to nil. The neural tuner was also implemented as a S-function NeuC_PI. Its inputs (inside the Subsystem "Tuner inputs") included current setpoint values for the armature current and speed control loops, actual value of the armature current and its delayed values, PI-controller output signal value. 6. Experimental results Two sets of experiments were conducted. In the first of them the armature current control loop PI-controller parameters were changed to the following values: KP = 1, KI = 10. The neural tuner task was to tune them back to the calculated values following all the transient quality requirements determined by the rules. The results obtained with the help of the control system without the tuner for such controller parameters values are shown in figure 4. As far as the drive speed is concerned, the time of the transients are about 0.0882 s and there is no overshoot. As for the armature current control, the overshoot is 20.6%. The results of the experiment with the neural tuner are shown in figure 5. The system has tuned the controller parameters back successfully. As far as the drive speed is concerned, the time of the transients are about 0.0725 s and there is no overshoot. As for the armature current control, the overshoot is 4.5%. So drive speed transients time was reduced by 17.8%, and overshoot in the armature current control loop was reduced by 16.1%. In addition to this one, such experiments with non-optimal PI-controller parameters were conducted with the following (KP, KI ) values: (0.06; 4.5), (0.06; 10), (1; 4.5). The neural tuner adjusted KP, KI back to the calculated values for all experiments. An average error was about 3%.
As for the second set of experiments, the PI-controller had its initially calculated values of the parameters (KP = 0.24, KI = 6.705). At the same time K parameter of the aperiodic link aperiod was increased by 40% over its nominal value. The neural tuner task was to find new PI-controller parameters, which are appropriate to the new situation. The results obtained with the help of the control system without the tuner are shown in figure 6. As far as the drive speed is concerned, the time of the transients are about 0.0707 s and there is no overshoot. As for the armature current control, the overshoot is 9.9%. The results of the experiment with the neural tuner are shown in figure 7. The system found new PIcontroller parameters. As far as the drive speed is concerned, the time of the transients are about 0.0551 s
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and there is no overshoot. As for the armature current control, the overshoot is 7.6%. So drive speed transients time was reduced by 22%, and overshoot in the armature current control loop was reduced by 2.3%. In both sets of the experiments the energy consumption of the system with the tuner became lower by 1-2% in comparison with the conventional control system.
Fig. 4. Results of the first set of the experiments for the control system without the tuner (upper graph – armature current Ia control loop, lower graph – drive speed n control loop).
Fig. 5. Results of the first set of the experiments for the control system with the neural tuner (the first graph – armature current Ia control loop, the second graph – drive speed n control loop).
7. Conclusion The PI-controller parameters neural tuner for armature current control loop of the two-high reversing rolling mill is developed as a result of the research work. Its usage is possible to solve two kind of problems. First of all, to tune
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PI-controller parameters, which are not appropriate at the moment. Secondly, to function permanently to follow the changes in the plant by adjusting existing controller parameters values. Its usage allows both to reduce time of the transients and rolling mill energy consumption. Future work is related to the problem of this tuner usage for speed control loop.
Fig. 6. Results of the second set of the experiments for the control system without the tuner (upper graph – armature current Ia control loop, lower graph – drive speed n control loop).
Fig. 7. Results of the second set of the experiments for the system with the tuner (the first graph – armature current Ia control loop, the second graph – drive speed n control loop).
Acknowledgments The research was performed with the support of the Russian foundation for basic research (Project #15-0706092) and Presidential Fund (Project #14.Y30.15.4865-MK).
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