Self-tuning Algorithm Research Articles

A study on water level control of PWR steam generator at low power and the self-tuning of its fuzzy controller

The water level control system of the steam generator in a pressurized water reactor and its control problems are analysed. In this work, a stable control strategy during low power operation and transient states is studied. The control strategy employs substitutional information using the bypass valve opening instead of incorrectly measured signal at the low flow rate as the fuzzy variable of the flow rate during low power operation, and includes the flexible scale adjusting method for fast response at a large transient. A practical self-tuning algorithm based on the control performance and the descent method is also suggested and applied to tuning the membership function scale of the flow error.

GH∞ Self-Tuning Control for Linear and Nonlinear Systems

Non-linear self-tuning is an alternative approach to robust control and to linear self-tuning for dealing with the non-linear uncertainties in a process. A novel general nonlinear self-tuning control algorithm is presented. An alternative non-linear adaptive solution is also described which deals with the nonlinear system characteristics by treating it as a form of uncertainty. The ability of GH ∞ control to handle the non-linear perturbations without the need to identify these nonlinearities is shown. This is a very good test for GH ∞ self-tuning showing its ability to cope with plant model uncertainties even those due to nonlinear characteristics.

A PID Instrument with Self-Tuning

A self-tuning algorithm for PID controllers that allows to specify closed-loop reference and disturbance responses is described. The algorithm consists of two phases, pretune and fine-tune, imitating the trials of field engineers who perform manual tuning. The first phase involves relay-generated oscillations, the latter one or two load disturbance rectangular pulse responses. Frequency, root-locus, and least-squares method are employed. The algorithm is implemented in RF-537 general purpose PlO control instrument.

ADAPTIVE MULTIVARIABLE PID CONTROLOF A DISTILLATION COLUMN WITH UNKNOWN AND VARYING DEAD TIME

Abstract A new, reliable, and easy-to-use adaptive control strategy has been developed to overcome the long-existing difficulties in adaptive control practice caused by unknown and varying process dead time. A self-tuning PID control algorithm is adopted to control a distillation column possessing second-order-plus-dead-time dynamics. The self-tuning strategy is based on recursive least-squares estimation of process parameters. U-D factorization is applied to stabilize the parameter estimation calculations. A variable forgetting factor is used to alleviate wind-up in the estimator. A simulation study and an experimental evaluation demonstrate the capability of the adaptive algorithm.

A new self-tuning algorithm for the frequency estimation of distorted signals

This paper introduces a new self-tuning digital signal processing algorithm for local power system frequency deviation measurement. The algorithm is derived using the nonrecursive least error square technique accompanied with an updating procedure, which generally improves the algorithm properties: the measurement range; the immunity to a random noise; and the accuracy. The algorithm developed takes into account the components of the fundamental frequency, the second through the M-th harmonics and a decaying DC component, so it could be used for the real-time distorted signals frequency estimation as well as for the harmonics measurement. To demonstrate the efficiency of the algorithm proposed, the results of the computer simulated, experimentally obtained and real-life data records tests are presented. >

Global convergent inferential self-tuning algorithm : Tian You Chai, Li Xin Zheng, pp 273–276

Global convergent inferential self-tuning algorithm : Tian You Chai, Li Xin Zheng, pp 273–276

State space based dual-rate self-tuning

A state space based dual-rate self-tuning algorithm is proposed for the case where the output sampling rate is slower than the input update rate. A state space innovation model suitable for dual-rate self-tuning control is derived. The parameters of the innovation model together with the states are estimated by a recursive prediction error estimator. The control strategy has been based on a pole restriction principle. Simulation studies are presented to establish the practical usefulness of the algorithm.

Generalized predictive control (GPC) with long-range predictive identification (LRPI) for multivariable anaesthesia

Early and current self-tuning algorithm formulations were generally based on the assumption that the process model under investigation is linear within a certain operating point. A standard Recursive Least-Squares (RLS) estimation algorithm was used to update the model parameters whose data input and output were normally fed through a filter with adequate characteristics. One of the most popular themes belonging to this class of adaptive controllers is that of generalized predictive control (GPC). Classified in the category of long-range predictive controllers (LRPC) its control law stems from the minimization of a cost function over a horizon which spans that used by the RLS algorithm (one step ahead). This paper describes a new approach which derives the same model parameters using extra filtering provided by an identification objective similar to the one used for control derivation. Already successfully applied in real-time to a SISO control system by its original authors, the scheme, known as long-ran...

Some Interpretations Of Self-Tuning Pid Controllers

One of the most recent development in the theories of adaptive methods in the form of self-tuning algorithms is in the area of self-tuning PID controllers (STPID). These controllers are a class of adaptive controllers but are essentially PID controllers with the capabilities of tuning their parameters automatically online. To this end, the theories of these types of controllers are still in the infancy stage. In this paper, we provide some interpretations of a STPID through some analytical and simulation results, thereby lending way for a better understanding of the algorithms and some insight into the usefullness of the algorithm. The interpretations also serve as an aid in the selection of the tuning parameters of this algorithm which can be a time consuming activity if done dilligently.

Self-tuning adaptive control of engine speed in the presence of random disturbances

This work deals with the control of engine speed systems that are subjected to unknown and time-varying external load disturbances, and have varying structural parameters due to the uncertainties of the system's performance. The conventional approach to the controller design of such system may not assure satisfactory control performance. A self-tuning adaptive control algorithm is presented and an on-line estimation strategy based on the recursive maximum likelihood method for the system parameters is developed. The estimation strategy updates the system dynamics to the adaptive controller, this enables the controller to control the engine speed system for the best transient and steady state response. Computer simulation results of the suggested estimation-adaptation algorithms, and comparative analysis of the adaptive scheme with the conventional control approach are presented.

Implementation of self-tuning algorithms for reference tracking of a DC drive using a DSP chip

This paper describes the implementation of two self-tuning control algorithms for the speed control of a permanent magnet DC motor. The algorithms minimize a cost function incorporating system input, output, and set-point variation for reference tracking. Variable forgetting factor using data normalization with constant trace has been utilized. Self-tuning controllers have been implemented using a single-chip digital signal processor (DSP). It results in reduction of system hardware, cost, and calculation time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A Construction of Self-Tuning PID Control System and Its Application to Temperature Control of a Polystyrene Reactor

Self-tuning control strategies have been considered for systems with unknown parameters. The rapid progress of digital computer enables us to simulate complex and large-scale systems. Many control techniques have been proposed in order to improve the control performance for discrete-time control systems. However, since these control techniques are too complex, it is usually difficult to apply them to real plants and also difficult to understand a physical meaning of control parameters included in these control systems. On the other hand, PID control methods based on the classical control theory have been used widely for real control systems since they have simple control schemes. Therefore, it is necessary to consider control methods with PID control structure in the discrete-time systems. Many literatures with regard to auto-tuning and self-tuning PID control strategies have been reported up to now. But none of them gives us any results for discrete-time systems with time-delay which takes important roles for the stability of the control system and the good control performance.In this paper, we consider a self-tuning PID control algorithm for a system with a time delay element. This control method has four gains, which are proportional, integral, differential gains and a gain for modifying the reference signal. This method enables us to improve a tracking property for a reference signal in the transient state, and also to guarantee the global stability of the control system. Finally, the effectiveness of this control method is shown by applying the method to a temperature control system of a polystrene reactor. In order to show the effectiveness of this method.

Adaptive control of basis weight and moisture content for a paperboard machine

Abstract The development and industrial application of a MIMO adaptive control strategy for a paperboard machine are investigated. The control strategy incorporates a conventional regulatory control technique, multivariable k-incremental predictor and self-tuning control algorithm. The pulp consistency and flow rate, and the steam pressure are simultaneously manipulated to control the reel basis weight and moisture content. The control system demonstrates a satisfactory performance. The variations in reel basis weight and moisture content are greatly reduced.

Tracking and disturbance rejection: perfect and imperfect

Abstract A method of designing a generalized minimum variance self-tuning controller with a special pole-zero assignment technique for setpoint tracking and disturbance rejection is presented. Both minimum phase and non-minimum phase systems are considered to achieve perfect and imperfect setpoint tracking and disturbance rejection, respectively. The problem of matching unity in H ∞-norm space is discussed, and a new method to match unity in another sense is developed. With parameter identification, an on-line self-tuning algorithm with the property of setpoint tracking and disturbance rejection is proposed. The effectiveness of the algorithm is shown by an example.

Global Convergent Inferential Self-Tuning Control Algorithm

A new inferential self-tuning control algorithm presented in this paper can deal with the systems with unmeasured controlled output. This control algorithm uses the generalized minimum variance control law and inferential strategy adopting double measurements as auxiliary outputs. The analysis of global convergence of this controller is also given.

Adaptive Control of a Flexible Manipulator with Varying Payload

This research is concerned with the control of the tip position of a flexible manipulator carrying variable payload. The tip of the manipulator considered here has three degrees of freedom, viz, one rotational and two translational. The desired rotational motion is the result of a command whereas the undesired translational motions are due to the deflection caused by the payload. Lagrangian equations with assumed mode shape functions are used to derive the dynamic equations of the manipulator. A recursive least square algorithm was used to identify the parameters. Numerical problems of the algorithm were overcome by introducing a scale factor and a time varying forgetting factor. The self-tuning adaptive control algorithm scheme was successfully implemented to control the rotational motion of the tip of the flexible manipulator carrying varying payloads. For the remaining two translational motions, the PID control algorithms were developed and fine tuned. The overall simulation results demonstrated the ability of the developed control system to maneuver the flexible manipulator so that the tip reaches the target position accurately under varying payloads.

The influence of configuration on controller design of multitubular reactors

The design, stability and control of multitubular fixed bed catalytic reactors are considered through a detailed mathematical model. An investigation of the interactions between coolant flow pattern and reactor behaviour suggests that new reactor configuration can improve control. Criteria are proposed for defining system operating conditions which can guarantee the reactor stability. Under these conditions the reactor can be very effectively controlled using self-tuning control algorithms.

Lab and field tests of a self-tuning power system stabilizer

Digital power system stabilizers have opened new avenues for applying innovative control strategies to damp generator rotor oscillations at steam and hydroelectric power plants. This paper describes test results from simulation studies and field tests where a self-tuning control algorithm was tested using a digital stabilizer. The simulation studies indicated a marked improvement in local mode damping and a contribution to damping at the inter-area mode while initial tests at power plants gave promising results. >

Real-time test results with adaptive speed controllers for a diesel prime-mover

Self-tuning adaptive control algorithms, that employ explicit dead-time identification techniques, have been discussed in the literature vis-a-vis the diesel-driven power prime-mover. Such algorithms are numerically intensive owing to the complicated identification algorithms. This paper discusses the implementation of the above mentioned algorithms on a Texas Instruments' DSP 320C30 based system. This digital signal processor has sufficient numerical capability to carry out all the real-time computations at sampling intervals of 10 ms. It can also perform well in the matters of speed measurement and control generation without any significant additional hardware. The results of real-time tests are compared with that obtained using the Woodward Governor Company's 701 prime-mover controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Convergence of a multi-input adaptive extremum controller

A self-tuning model-based approach to extremum control described where a number of control variables can be manipulated to maximise a scalar output. A number of possible estimation models are considered and the local convergence properties of the resulting self-tuning algorithms are investigated.