Self-tuning Control Systems Research Articles

OWN MOTION FILTERS OPTIMISATION FOR SELF-TUNING ACS WITH THE TECHNOLOGICAL TYPE OF CONTROL OBJECT

Technological processes as control objects are characterized by substantial delay in controlled variables response to control action and considerable quantity of varying factors, which have influence on the process, but, practically, inaccessible for measurement. These specifics, in practice, often lead to substantial decrease in operation quality of the control system with typical algorithms and significantly make it harder to design and develop self-tuning control systems for these types of control objects. Factors, depending on the sequences of the impacts they have on the control object, are subdivided into non-controllable coordinate and parametric disturbances. In this article the case is considered, when spectral composition of parametric disturbances is substantially lower in frequency range in comparison to coordinate disturbances spectral composition. Factors, which cause high-frequency changes in controlled variables, which cannot be compensated by the control actions, are considered as noises. For control objects of technological type, in which transition coefficient changes under the influence of parametric disturbances, the structure of self-tuning automatic control system is proposed. Unlike many known, principle of its operation includes passive identification of the transition coefficient changes in the closed-loop circuit. From the overall movement of the closed-loop system, which arises under the influence of the coordinate disturbances, the part of the own motion of the system is being separated utilizing band pass filters. By detecting the changes in spectrum of this component, self-tuning loop determines current value of control object transition coefficient and changes the value of controller transition coefficient in the stabilization circuit for maintaining the stable operation of the system. Computer experiments for evaluation of how coordinate disturbances and noises spectral composition change influence the quality of self-tuning were carried out, the possibility of carrying out the optimal parametric synthesis of the system is shown, recommendations for approximate evaluation of the self-tuning loop parameters are proposed.

Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems

Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems

Memory-saving memetic computing for path-following mobile robots

In this paper, a recently proposed single-solution memetic computing optimization method, namely three stage optimization memetic exploration (3SOME), is used to implement a self-tuning PID controller on board of a mobile robot. More specifically, the optimal PID parameters minimizing a measure of the following error on a path-following operation are found, in real-time, during the execution of the control loop. The proposed approach separates the control and the optimization tasks, and uses simple operating system primitives to share data. The system is able to react to modifications of the trajectory, thus endowing the robot with intelligent learning and self-configuration capabilities. A popular commercial robotic tool, i.e. the Lego Mindstorms robot, has been used for testing and implementing this system. Tests have been performed both in simulations and in a real Lego robot. Experimental results show that, compared to other online optimization techniques and to empiric PID tuning procedures, 3SOME guarantees a robust and efficient control behaviour, thus representing a valid alternative for self-tuning control systems.

Design of Performance Driven Self-Tuning PID Control Systems and It's Application for DC-DC Converters

Design of Performance Driven Self-Tuning PID Control Systems and It's Application for DC-DC Converters

On the stability and convergence of self-tuning control–virtual equivalent system approach

This article presents a unified understanding and judgement of the stability and convergence of a general self-tuning control (STC) system, which consists of arbitrary control strategy, arbitrary parameter estimation algorithm and a deterministic/stochastic linear time-invariant (LTI) plant. The necessary conditions required for the global stability and convergence of a general STC system are relaxed, i.e. the convergence of parameter estimates is removed for both deterministic and stochastic STC schemes. To reach this goal, ‘virtual equivalent system (VES)’ concept and methodology is adopted. With the help of VES, the original nonlinear dominant (nonlinear in structure) problem is converted to a linear dominant (linear in structure) problem. The results developed in this article show that STC systems are stable and convergent for the abundance of control strategies and parameter estimation algorithms, which will provide great flexibility in the applications of STC.

Self-tuning control systems of decentralised velocity feedback

This paper is concerned with decentralised velocity feedback for the control of vibration on a flexible structure. Previous studies have shown that a direct velocity feedback loop with a collocated force actuator produces a damping action. Multiple velocity feedback control loops thus reduce the vibration and sound radiation of structures at low frequency resonances, where the response is controlled by damping. However, if the control gains are too high, so that the response of the structure at the control point is close to zero, the feedback control loops will pin the panel at the control positions and thus no damping action is generated. Therefore, in order to maximise the active damping effect, the feedback gains have optimum values and the loops need to be properly tuned. In this paper, a numerical investigation is performed to investigate the possibility of self-tuning the feedback control gains to maximise the power absorbed by the control loops and hence maximise the active damping. The tuning principle is first examined for a single feedback loop for different excitation signals. The tuning of multiple control loops is then considered and the implementation of a practical tuning algorithm is discussed.

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.

A gain scheduled PID control method based on nonlinear model predictive control

This paper tries to design self-tuning PID control systems for non-linear systems, which optimize a certain performance index. The key idea of this research is to utilize non-linear model predictive control method in order to design the PID gains. After designing the closed loop systems by using PID controller, the PID gains are regarded as manipulated variables. Then, non-linear model predictive control systems are designed where the performance index consists of integrated quadratic forms of tracking error and manipulated variables representing PID gains. The proposed controller is categorized as gain-scheduled controller because the PID gains are tuned using state variables as scheduling parameter. That is why we call it the gain scheduled PID controller. The effectiveness of the proposed method is shown by comparative simulation results with the fixed gain PID control systems.

Neural Networks for Self-tuning Control Systems

In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications.

Self-Tuning Control: Laboratory Real - Time Education

The combination of the automatic control theory courses and practical implementation of the designed controller algorithms in real-time conditions is very important for training of the control engineers. This contribution presents a MATLAB-Toolbox for design, simulation verification and especially real-time implementation of single input - single output (SISO) discrete self-tuning controllers. The proposed adaptive controllers what are included into a Toolbox can be divided into three groups. The first group covers PID adaptive algorithms using traditional Ziegler-Nichols method for the setting of the controller parameters, the second group of described controllers is based on the pole placement design and the third group contains the controllers derived on the other approaches (dead-beat, minimum variance etc.). The controllers are implemented as an encapsulated SIMULINK blocks and thus allows users simple integration into existing SIMULINK schemas. The process of developing real-time applications using MATLAB Real-Time Workshop and several control courses is also presented. The MATLAB-Toolbox is very successfully used in Adaptive Control Course in education practice for design and verification of self-tuning control systems in real-time. It is suitable for design and verification of the industrial controllers, too. The MATLAB-Toolbox is available free of charge at Internet site - http://www.utb.cz/stctool/.

A design of self‐tuning pole‐assignment control systems based on minimizing quadratic cost functions

AbstractThere are numerous studies of the design of self‐tuning control (STC) systems for linear nonminimum‐phase systems. A wide variety of existing methods on designing STC systems are conceptually classified into two groups. One is based on minimization of an error criterion, and the other on pole assignment of the closed‐loop system. On the other hand, in order to guarantee the tracking property for the reference signals, an internal model which corresponds to the reference signals must be inserted into the control system based on the internal model principle. However, the variance of the control error signal may become large on inserting the internal model, because stochastic noise may be amplified. In this paper, a design method for self‐tuning pole‐assignment control systems with internal models is proposed based on minimizing the linear quadratic cost function. The newly proposed control scheme has the feature that the variance of the stochastic noise in the control system is reduced owing to the minimum variance control scheme, and also the tracking property with respect to the reference signal is assured owing to the internal model. The proposed control scheme is numerically evaluated in some simulation examples. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(1): 50–57, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10120

A Design of Self-Tuning PID Control Systems Based on Pole-Assignment Method

PID control schemes have been widely used for various process control systems for a long time. However, since such processes have relatively large time-delays and uncertainties caused by modeling errors and system fluctuation, it is non-trivial to choose a suitable set of PID parameters. In this paper, a design scheme of self-tuning PID controllers is proposed for unknown processes with time-delays. This scheme is equipped with a parallel feedforward compensator to compensate for the time-delays. The PID control system is then designed for the augmented system. The PID control parameters are calculated adaptively based on on-line process identification and the relationship between the minimum variance control law and PID control law.

Design of Self-Tuning Control Systems for Utilization

Design of Self-Tuning Control Systems for Utilization

最小分散を考慮したセルフチューニング極配置制御系の一設計

There are numerous studies of the design of self-tuning control (STC) systems for linear nonminimum-phase systems. A wide variety of existing methods on designing STC systems are conceptually classified into two groups. One is based on minimization of an error criterion, and the other on pole assignment of the closed-loop system. On the other hand, in order to guarantee the tracking property for the reference signals, an internal model which corresponds to the reference signals must be inserted into the control system based on the internal model principle. However, the variance of the control error signal may become large on inserting the internal model, because stochastic noise may be amplified. In this paper, a design method for self-tuning pole-assignment control systems with internal models is proposed based on minimizing the linear quadratic cost function. The newly proposed control scheme has the feature that the variance of the stochastic noise in the control system is reduced owing to the minimum variance control scheme, and also the tracking property with respect to the reference signal is assured owing to the internal model. The proposed control scheme is numerically evaluated in some simulation examples. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(1): 50–57, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10120

A Design of Multiloop Predictive Self-Tuning PID Controllers

In this paper, a design scheme of multiloop predictive self-tuning PID control systems is proposed for multivariable systems. The proposed scheme firstly uses a static pre-compensator as an approximately decoupling device, in order to roughly decouple the controlled object. Furthermore, by regarding the approximately decoupled system as a series of single-input single-output subsystems, a single-input single-output PID controller is designed for each subsystem. The PLD parameters are calculated on-line based on the relationship between the PID control and the generalized predictive control laws.

A Design of Neural-Net Based Generalized Minimum Variance Controller

Much research on designing self-tuning control systems for linear systems have been proposed by using the least squares parameter identification method. However, it is difficult to employ the algorithm for the nonlinear systems except for the case where the unknown parameters are linearly combined with nonlinear terms. In this paper, a parameter estimation scheme for nonlinear systems is proposed by using a neural network. Furthermore, a design method of the control system is derived by minimizing a cost function of the generalized minimum variance control. This control input is calculated by using the estimated parameters. Finally, the effectiveness of the proposed scheme is numerically evaluated.

Tuning of a neuro-fuzzy controller by genetic algorithm

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.

Model-based control of strip temperature for the heating furnace in continuous annealing

Presents the model of the heating furnace in continuous annealing processes for use in design of self-tuning control systems. A simplified mathematical model is derived from first principles. The model parameters are recursively estimated with an algorithm called recursive parameter estimation with a vector-type variable forgetting factor (REVVF). The REVVF algorithm was developed for such cases where some knowledge on parameter variability can be obtained beforehand. The control system of strip temperature presented here is hierarchical. The upper level is called "optimal preview control", which performs preset control. It previews the approaching setup change, which is the change of strip size or reference temperature, and optimizes the line speed and the strip temperature trajectory. Next, the lower level is called "temperature tracking control", which performs closed-loop control using the above trajectory as the control target. At this level, the generalized pole-placement self-tuning control was first employed; and later, the generalized predictive self-tuning control was introduced. These control methods were applied with some practical modifications and with the above mentioned REVVF. The control has been working successfully in several real plants.

セルフチューニング極配置制御系における過渡特性の一改善法

セルフチューニング極配置制御系における過渡特性の一改善法

Designs of Self-tuning Control Systems for Ships

This paper treats of the applications of the pole assignment self-tuning control theory to the identification and design of ship's auto-pilot systems. An on-line estimation algorithm known as the Recursive Least Squares Algorithm is used to identify parameters of the control system. Two typical types of the pole assignment self-tuning control algorithms that can be linked to the on-line estimation algorithm to form self-tuning control systems for ships are introduced. The authors discuss how to choose the control parameters by the rules of thumb in the actual sea trials. The pole assignment self-tuning controllers designed to steer ships are verified by experiments aboard the training ship Shioji Maru of Tokyo University of Mercantile Marine. The aboard experiments result in the confirmation that it is possible to keep and to change the ship's course as desired by the designed controllers.