Self-tuning Control System Research Articles

Self-Tuning Type-2 PID control system and its application

This study proposes an adaptive control method of a weigh feeder. A weigh feeder dispenses material into a process at a precise rate, and it has been employed in industries, e.g., process control, cement manufacturing plants, food industry equipment, and so on. To introduce advanced control into industries, self-tuning controllers are designed for controlling a weigh feeder. Three difference controllers are designed; one degree-of-freedom (1DOF) PID, 1DOF PD, and two degree-of-freedom (2DOF) PD controllers, and these control methods are compared through experimental results. Because discharged mass is measured by employing loss-in-weight method, a reference input followed by a plant output is ramp-type, and a type-2 control system has to be designed. Since the controlled object includes an integrator, a type-2 control system can be obtained by using 1DOF PID controller. In design of 2DOF PD control, a pre-compensator is designed to eliminate steady-state velocity error. Further, to be compared with 1DOF PID and 2DOF PD control, a 1DOF PD controller is designed. In this paper, PID and PD controllers are designed on the basis of generalized minimum variance control (GMVC) to obtain useful control methods adopted in industries. In design of the proposed control methods, GMVC can be replaced precisely with a simple PID or PD controller, and advanced control performance can be obtained. Experimental results are shown and compared, and the effectiveness of the proposed design method is shown.

POLE-ASSIGNMENT BASED SELF-TUNING PID CONTROLLERS FOR MULTIVARIABLE SYSTEMS WITH UNCERTAIN TIME-DELAYS

PID control schemes have been widely still used in process industries. However, the good control performance can not be obtained by simply using PID control schemes, since most processes are considered as multivariable systems with mutual interactions, time variation, and uncertain time-delays. In this paper, pole-assignment based self-tuning PID control system is newly proposed for multivariable systems whose system parameters and time-delays can not be exactly obtained beforehand. Then, the proposed PID control system is composed of a static decoupler, improved Smith compensators, pole-assignment based PID gain tuners and recursive parameter estimators. Finally, the effectiveness of the proposed control scheme is evaluated on a simulation example.

A NEW METHODOLOGY TO THE CONTROL PROBLEM OF HORIZONTAL AXIS WIND POWER PLANTS USING ADAPTIVE NEURAL NETWORK

The main goal of this study is to afford an involvement to the control problem of horizontal axis wind power plants by means of neural network approach. The current paper is a part of a research plan to study the dynamics and control of horizontal axis wind turbines. This work presents a novel methodology to control wind power plants. It makes use of an adaptive neural networks self-tuning control system of medium scale wind turbine system, through different operating conditions. The planned control system consists of neural networks forward and inverse identifiers, which are used to model their dynamics, and to adapt neural controller parameters. A reference model is used to enhance the training course and neural controller which is used to produce control signal to the pitch angle actuator. The planned control system carry out high-quality performance which reveal that the proposed control system is in fact an innovative contribution in the control field of horizontal axis wind turbine power generation systems judge against with previous works.

An architecture-adaptive neural network online control system

An architecture-adaptive intelligent self-tuning control system is presented. The system is composed of the supervisor module, the model refinement module, the process plant and the database. In the supervisor module, the user prescribes the desired curve for the plant dynamic process. The model refinement module is in parallel with the process plant, and consists of the self-tuning process model, which contains an architecture-adaptive neural network. The model refinement module could learn intelligently the real process plant by the prompt adjustments based on the difference of the outputs of the two modules, and its learned model is also refined gradually. This diagram is especially versatile in the complex nonlinear and time-variant systems in practice.

Fuzzy-Logic-Based Self-Tuning PI Controller for Speed Control of Indirect Field-Oriented Induction Motor Drive

The indirect field-oriented (IFO) method of induction motor (IM) drive is generally implemented by the fixed gain PI controller because of its simple design technique. The performance of fixed gain PI controller may be well under some operating conditions, but not all desired operating conditions. To increase the performance of PI controller, we propose a new fuzzy-logic-based self-tuning PI control system for speed control of IM drives with IFO method. In this new approach, a well designed fuzzy-logic provides the suitable gain of PI controller to achieve the desired speed under the variation of load torque and parameters. The performance of proposed fuzzy-logic-based self-tuning PI controller was demonstrated through simulations. The simulation results confirm that the proposed controller is robust under the variation of load torque and parameters.

On the performance enhancement of self-tuning adaptive control for time-varying machining processes

This paper investigates efficient approaches to improve the performance of a self-tuning adaptive control system for time-varying machining processes. The milling process is a typical time-varying system because of variations of the cutting conditions, e.g., the change of cutting depths and variation of the cutting materials. On the other hand, the milling processes are considered as typically non-minimum phases since one or more zeros of discrete-time models of milling processes may be located outside the unit circle. In this paper, an efficient method, using the pole assignment method, is presented to design a self-tuning adaptive controller for time-varying and non-minimum phase milling processes, and an effective method to select appropriate design parameters in order to improve its performance has been proposed. Its effectiveness has been verified in experiments on controlling milling processes with varying cutting depth. The experimental results illustrate that improved control performances can be obtained using the appropriate design parameters selected according to the principles and criteria presented here.

A Self-Tuning PI control system design for the flatness of hot strip in finishing mill processes

A novel flatness sensing system which is called the Flatness Sensing Inter-stand Looper (FlatSIL) system is suggested and a self-tuning PI control system using the FlatSIL is designed for improving the flatness of hot strip in finishing mill processes. The FlatSIL system measures the tension along the direction of the strip width by using segmented rolls, and the tension profile is approximated through the tension of each segmented roll. The flatness control system is operated by using the tension profile. The proposed flatness control system as far as the tension profile-measuring device works for the full strip length during the strip rolling in finishing mills. The generalized minimum variance self-tuning (GMV S-T) PI control method is applied to control the flatness of hot strip which has a design parameter as weighting factor for updating the PI gains. Optimizing the design parameter in the GMV S-T PI controller, the Robbins-Monro algorithm is used. It is shown by the computer simulation and experiment that the proposed GMV S-T PI flatness control system has better performance than the fixed PI flatness control system.

A design of multivariable, self‐tuning PID controllers with an internal model structure

AbstractSelf‐tuning control schemes (STC) are useful for systems with unknown or slowly time‐varying parameters. Some single‐input/single‐output PID control schemes based on STCs have been proposed for such systems. However, there are a lot of multivariable systems in real process industries. And these systems often have relatively large time delays. In this paper, a design scheme of self‐tuning PID control system is proposed for multivariable systems with unknown parameters and time delays. The controlled object is equipped with an internal model in order to compensate the time delay and also unstable zeros. Subsequently, a multivariable PID controller is designed for the augmented or compensated system. The PID parameters are calculated recursively based on the relationship between the minimum variance control law and the PID control law. A simulation example is presented to demonstrate the effectiveness of the proposed scheme. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 146(4): 58–64, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10241

Self-tuning fuzzy control with a grey prediction for wire rupture prevention in WEDM

Wire rupture in the wire electrical discharge machining (WEDM) process is one of the most troublesome problems in practical applications. In this paper, the abnormal ratio Rab, defined as the proportion of abnormal sparks in a sampling period, is taken to represent the gap state in machining. The grey predictor is adopted to compensate the time-delayed Rab caused by the low pass filter data processing. A gain self-tuning fuzzy control system has been developed to cope with the conditions that often occur with wire rupture in the WEDM process, such as an improper setting of machining parameters, machining the workpiece with varying thickness, etc. Experimental results of several cases show that the proposed controller results in a satisfactory performance. Not only can it immediately suppress transient situation once there is a sudden change of workpiece thickness, but a stable performance can also be achieved during machining a workpiece of constant thickness. As a result, wire rupture problems in most WEDM processes can be successively solved by the proposed control strategy.

A stable self-tuning fuzzy logic control system for industrial temperature regulation

A closed-loop control system incorporating fuzzy logic has been developed for a class of industrial temperature control problems. A unique fuzzy logic controller (FLC) structure with an efficient realization and a small rule base that can be easily implemented in existing industrial controllers was proposed. The potential of FLC in both software simulation and hardware tests in an industrial setting was demonstrated. This includes compensating for thermo mass changes in the system, dealing with unknown and variable delays, operating at very different temperature set points without retuning, etc. It is achieved by implementing, in the FLC, a classical control strategy and an adaptation mechanism to compensate for the dynamic changes in the system. The proposed FLC was applied to two different temperature processes and performance and robustness improvements were observed in both cases. Furthermore, the stability of the FLC is investigated and a safeguard is established.

内部モデル構造を有した多変数セルフチューニングPID制御系の一設計

Self-tuning control schemes (STC) are useful for systems with unknown or slowly time-varying parameters. Some single-input/single-output PID control schemes based on STCs have been proposed for such systems. However, there are a lot of multivariable systems in real process industries. And these systems often have relatively large time delays. In this paper, a design scheme of self-tuning PID control system is proposed for multivariable systems with unknown parameters and time delays. The controlled object is equipped with an internal model in order to compensate the time delay and also unstable zeros. Subsequently, a multivariable PID controller is designed for the augmented or compensated system. The PID parameters are calculated recursively based on the relationship between the minimum variance control law and the PID control law. A simulation example is presented to demonstrate the effectiveness of the proposed scheme. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 146(4): 58–64, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10241

Neural-network-based self-tuning PI controller for precise motion control of PMAC motors

In general, proportional plus integral (PI) controllers used in computer numerically controlled machines possess fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed-gain PI controllers, we propose a new neural-network-based self-tuning PI control system. In this new approach, a well-trained neural network supplies the PI controller with suitable gain according to each operating condition pair (torque, angular velocity, and position error) detected. To demonstrate the advantages of our proposed neural-network-based self-tuning PI control technique, both computer simulations and experiments were executed in this research. During the computer simulation, the direct experiment method was adopted to better model the problem of hysteresis in the AC servo motor. In real experiments, a PC-based controller was used to carry out the control tasks. Results of both computer simulations and experiments show that the newly developed dynamic PI approach outperforms the fixed PI scheme in rise time, precise positioning, and robustness.

Swing Control of a Variable-Length Pendulum Using Self-Tuning Regulator.

This paper proposes a control method for the swing of a variable-length pendulum. Because Coriolis' force is caused by a compound motion (rotational and rectilinear) the pendulum is governed by non-liner equation of motion, even if the swing angle is small. In this paper, we developed a self-tuning control system utilizing the Coriolis force. The system is controlled by using an output equation including a non-liner term as a system parameter to be estimated. The control law is based on the minimization of a quadratic cost function. Using the newly propose control scheme, the pendulum can be controlled without knowing information about the system parameters and the phase angle information between the swing angle of the pendulum and its velocity. The experimental and useful for controlling the swing of the pendulum compared with the conventional method such as Lyapunov's method.

Synthesis of Underwater Robot's Adaptive Velocity Control System

Self-tuning underwater robot's control system at the stepwise varies of its velocity is syntheses in this paper. This control system based on variable-structure system which working in sliding mode and allow to carry out self-tuning of a control system without directly measuring current parameters of the control object. Investigation of developed control system was carry out, which showed efficacy its control system.

An Application of Self-Tuning Fuzzy Controller to Dynamic Positioning System of Floating Production System

Abstract Position-keeping of a floating body is a very important matter in a production system in the deep sea. How a floating body can be kept stationary is a key problem in this study. Conventionally, the mooring system is adopted to position a floating body under disturbance of wind, wave, and current; but, in general, it is difficult to survive storm disturbance in the deep sea. The dynamic positioning system can solve this problem, but thrusters must be operated at all times against random drift force of wave, wind, and current, and a great deal of energy is required. So a composite system of single-point mooring system and fuzzy control dynamic positioning (FDP) system was studied in a previous paper (Inoue, 1994). However, up to now in almost all kinds of dynamic positioning systems, control strategy must be decided in advance. This is unreasonable under a random condition. In this paper, a self-tuning fuzzy control system is put forward. By this approach, the control precision of the previous fuzzy dynamic positioning is fairly improved. The main concept of this approach is that at first a feasible control strategy is decided in advance, then during the motion of the system, the control strategy is improved automatically according to the sea conditions of operation. This system is more reasonable than the previous system because, in fact, the disturbance of wave, current, and wind cannot be predicted.

Adaptive Time Optimal Model Reference and Sensitivity Based Control of Servosystems

A structure of a self-tuning time optimal control system which achieves a good and fast adaptation by using a reference model and a sensitivity model is described- A procedure of determination fhe time optimal control algorithm parameters by using the reference model and the sensitivity model is elaborated. A way of including an unmodelled dynamics and external disturbance is proposed. Simulation results indicate that the proposed structure provides very good adaptation to variations of system parameters within only two to three stepwise changes of the referent input.

ニューラルネットワークによるセルフチューニングPID制御系の設計

ニューラルネットワークによるセルフチューニングPID制御系の設計

The application of neural networks in self-tuning constant force control

The constant force control gradually becomes an important technique of modern manufacturing processes. For example, the constant turning or cutting force is a useful approach for increasing the metal removal rate and increasing the tool life. The variation of machining condition may cause the robustness of a classical control theory (PID) to become ineffective, even make a control system unstable. The pole placement self-tuning control (PSTC) theory with a recursive least square parameters estimator is proposed to adapt the environmental variety. Unfortunately, the adaptability and the robustness of a self-tuning control system cannot be maintained in good condition simultaneously all the time. In this paper, a self-tuning controller equips with a neural network parameter classifier in conjunction with a least square estimator is developed to improve the adaptability and the robustness in suffering the obvious environmental variation. In order to verify the applicability of this control method, a prototype system is designed and constructed to resemble the feed rate mechanism of lathe. The dynamic responses of this force control system with different estimators are compared based upon the experimental data. The contact force is measured from a load cell and adjusted by regulating the feed rate.

A Simple Pole-Assignment Scheme for Designing Multivariable Self-Tuning Controllers

Lots of self-tuning control schemes have been proposed for unknown parameter systems. Among them, pole-assignment schemes have been widely used for unknown time delay systems. They are usually classified into two methods, the implicit and the explicit methods according to how to identify the parameters. The latter has an advantage to design a control scheme by taking account of the stability margin and control performance. However, it involves a considerably computational burden to solve a Diophantine equation. A simple scheme is proposed in this paper, which can construct a self-tuning pole-assignment control system, while taking account of the stability margin and control performance without solving the Diophantine equation.

Self-Tuning Fuzzy Control for Dynamic Positioning System of Offshore Structures

In this paper, a self-tuning fuzzy control system is proposed. This approach can improve the control precision of the previous fuzzy dynamic positioning (FDP) (Inoue, 1994) considerably. The main concept of this approach is that a transient control strategy is decided in advance, during the motion of system, the control strategy is modified automatically according to the environment. This system is more reasonable than the previous system because in fact the disturbance of wave, current and wind cannot be predicted in advance. In order to test the system, simulations are carried out. The results show this system is better than the dynamic positioning system (DPS) with simple fuzzy control.