Self-tuning Research Articles

Robust Intelligent Self-Tuning Active Force Control of a Quadrotor With Improved Body Jerk Performance

This work presents an innovative hybrid control scheme for a quadrotor unmanned aerial vehicle (UAV) model to improve the disturbances rejection capability and body jerk performance by utilizing an active force control (AFC)-based robust intelligent control system via a simulation study. The proposed intelligent control approach incorporates a proportional-integral-derivative (PID) and an intelligent active force control (IAFC) element yielding a robust PID-IAFC scheme. A detailed mathematical model of a quadrotor system with six degrees of freedom (DOFs) was first derived using the Newton-Euler method taking into consideration the gyroscopic terms, disturbances, aerodynamics, and friction effects. In the derived model, the PID controller was first designed to stabilize the quadrotor model and achieve the required altitude and attitude motions. In addition, different types of external disturbances in the form of sinusoidal waves and repeated impulses (pulsating) were added. An AFC strategy, known as PID-AFC, was designed and incorporated into the PID controller, and was initially tuned heuristically. Then, an artificial intelligence (AI)-based method employing an iterative learning (IL) algorithm was designed and implemented into the AFC (ILAFC) to estimate the control parameters automatically while on-line. Thereafter, the performance of the ILAFC was compared to the AFC with fuzzy logic (FL) which became known as FLAFC. Also, a self-tuning (ST) PID controller was designed and employed based on the FL method to automatically tune the PID gains based on the prescribed operating and loading conditions. Moreover, a comparative study of the system performance was carried out utilizing the PID, PID-AFC, ILAFC, FLAFC, and ST-FPID-AFC schemes to analyze the system characteristics. Furthermore, the effectiveness of the AFC-based intelligent controller was investigated in connection with the body jerk performance in the presence of external disturbances. The simulated results reveal the effectiveness and robustness of the proposed control strategy based on the IAFC technique in improving the disturbance rejection capability and body jerk performance by 17% in the presence of uncertainties and external disturbances.

Fusion identification and estimation of multisensor multichannel AR signals with missing measurements and sensor biases

This paper studies the problem of fusion identification and estimation for multisensor multichannel autoregressive (AR) signals with unknown missing measurement rates, model parameters (MPs), sensor biases and measurement noise variances. Optimal local filters, cross-covariance matrices (CCMs), and a distributed fusion filter for AR signal are presented when the system model is accurately known, respectively. A self-tuning (ST) fusion filter is presented when MPs of AR signal, sensor biases, missing measurement rates and measurement noise variances in the system model are unknown. First, MPs of AR signal and sensor biases are identified by using a multi-dimensional recursive extended least squares (MRELS) algorithm. Further, distributed fusion estimates of MPs of AR signal are obtained by using a matrix-weighted optimal fusion estimation algorithm in the sense of linear unbiased minimum variance (LUMV). Then, receiving measurement rates and measurement noise variances are identified by correlation functions. Finally, substituting the identified parameters into the proposed optimal filtering algorithms, a matrix-weighted self-tuning (ST) fusion filtering algorithm is obtained. An example verifies the effectiveness of algorithms.

Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters.

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives

<span>Fuzzy logic controller has been the main focus for many researchers and industries in motor drives. The popularity of Fuzzy Logic Controller (FLC) is due to its reliability and ability to handle parameters changes during load or disturbance. Fuzzy logic design can be visualized in two categories, mamdani design or Takagi-Sugeno (TS). Mamdani type can facilitate the design process, however it require high computational burden especially with big number of rules and experimental testing. This paper, develop Self-Tuning (ST) mechanism based on Takagi-Sugeno (TS) fuzzy type. The mechanism tunes the input scaling factor of speed fuzzy control of Induction Motor (IM) drives Based on the speed error and changes of error. A comparison study is done between the standard TS and the ST-TS based on simulations approaches considering different speed operations. Speed response characteristics such as rise time, overshoot, and settling time are compared for ST-TS and TS. It was shown that ST-TS has optimum results compared to the standard TS. The significance of the proposed method is that, optimum computational burden reduction is achieved.</span>

Application of an Additive Self-tuning Controller for Static Synchronous Series Compensator for Damping of Sub-synchronous Resonance Oscillations

In this paper, an additive self-tuning (ST) control scheme is presented for a static synchronous series compensator (SSSC) to improve performance of conventional PI control system for damping sub-synchronous resonance (SSR) oscillations. The active and reactve series compensation are provided by a three-level 24-pulse SSSC and fixed capacitor. The proposed ST controller consists of a pole shift (PS) controller and a recursive least squares (RLS). The RLS identifier algorithm is used to estimate parameters of auto regressive moving average exogenous (ARMAX) model. With this scheme, there is no any necessity for retuning the PI controller parameters under different operational circumstances of the system. The SSR analysis are performed using eigenvalue analysis, transient simulation and FFT analysis. The considered test system is adapted from IEEE First Benchmark Model. As shown in the results, the proposed additive ST controller has an effective and acceptable performance for damping of SSR under different disturbances and operating conditions. It should be noted here that all digital simulations have been done by using MATLAB.

Design of an Improved Nonlinear H∞ Control for UPFC to Enhance Transient Stability of Power System

An improved nonlinear (IN) H∞ control of unified power flow controller (UPFC) is investigated and designed in the power system by using the Hamiltonian function method. First, the dissipative Hamiltonian structure of the UPFC system is established by means of variable transformation. Then, based on the obtained dissipative Hamiltonian structure, an IN H∞ control is put forward. Simulation results on the two-area four-machine power system demonstrate the effectiveness and robustness of the IN H∞ control in comparison with that of feedback linearization control (FLC) and add-on self-tuning (ST) control. The research results show that the IN H∞ control has better performance than the two other controls.

Self-tuning virtual synchronous generator control for improving frequency stability in autonomous photovoltaic-diesel microgrids

This paper investigates the use of a virtual synchronous generator (VSG) to improve frequency stability in an autonomous photovoltaic-diesel microgrid with energy storage. VSG control is designed to emulate inertial response and damping power via power injection from/to the energy storage system. The effect of a VSG with constant parameters (CP-VSG) on the system frequency is analyzed. Based on the case study, self-tuning algorithms are used to search for optimal parameters during the operation of the VSG in order to minimize the amplitude and rate of change of the frequency variations. The performances of the proposed self-tuning (ST)-VSG, the frequency droop method, and the CP-VSG are evaluated by comparing their effects on attenuating frequency variations under load variations. For both simulated and experimental cases, the ST-VSG was found to be more efficient than the other two methods in improving frequency stability.

Self-Tuning Virtual Synchronous Machine: A Control Strategy for Energy Storage Systems to Support Dynamic Frequency Control

This paper investigates the use of a virtual synchronous machine (VSM) to support dynamic frequency control in a diesel-hybrid autonomous power system. The proposed VSM entails controlling the grid-interface converter of an energy storage system (ESS) to emulate the inertial response and the damping power of a synchronous generator. In addition, self-tuning algorithms are used to continuously search for optimal parameters during the operation of the VSM in order to minimize the amplitude and rate of change of the frequency variations and the power flow through the ESS. The performances of the proposed self-tuning (ST)-VSM and the constant parameters (CP)-VSM were evaluated by comparing their inertial responses and their damping powers for different scenarios of load variations. For the simulated cases, the ST-VSM achieved a similar performance to that of the CP-VSM, while reducing the power flow through the ESS in up to 58%. Moreover, in all the simulated scenarios, the ST-VSM was found to be more efficient than the CP-VSM in attenuating frequency variations, i.e., it used less energy per Hertz reduced.

An Add-On Self-Tuning Control System for a UPFC Application

This paper presents an add-on self-tuning (ST) control scheme for a Unified Power Flow Controller (UPFC) to assist its conventional PI control system in damping power oscillations. The ST control algorithm is based on a Pole Shift (PS) technique that has previously been successfully applied in Adaptive Power System Stabilizers (APSSs). For a wide range of operating conditions, the conventional PI-UPFC unless returned suffers from insufficient/nonoptimal damping performance. To overcome this problem, the authors propose supplementing the PI controllers with an ST feedback loop comprised of an identifier and a PS control algorithm. With a CRLS identifier tracking the system conditions online, this scheme eliminates the need for PI parameter retuning. Further, to correctly identify system parameters during large disturbances, a constrained recursive least squares (CRLS) identification procedure is adopted here. An improved damping performance with the proposed add-on scheme assisting the nonoptimal PI-UPFC is demonstrated for a two-area power system.

Stochastic resonance and self-tuning: A simple threshold system

To put it in the framework of information processing, stochastic resonance (SR) is a phenomenon in which the transfer of information from input to output signals can be significantly increased by noise with appropriate (nonzero) intensity. The minimum requirements for SR are generally input signals, nonlinearity, and noise, in addition to a quantifier to measure the efficiency of information transfer. We study a simple threshold system and propose an adaptation mechanism for system parameters, such as a threshold and noise intensity (i.e., temperature), which are held fixed in a normal SR setting. Our emphasis is put on parameter dynamics induced by this adaptation, which we call self-tuning (ST), and on how this dynamics modifies or changes the SR picture with respect to information processing efficiency. As a measure for performance of the threshold system we take mutual information and the signal-to-noise ratio. These quantities are calculated by an analytical method and by a simulational one. ST of temperature results in oscillatory time variation in temperature with its average located around the temperature at which performance is maximized in the SR setting. ST of a threshold turns out to improve performance in the weak noise region.

Self-tuning control of dead time compensation strategy for PMSM control

A self-tuning predictive control strategy for a permanent magnet synchronous motor (PMSM) drive with dead time is proposed. Firstly, a dead time compensator (DTC), based on the Smith predictor (SP) and a self-tuning (ST) proportional-integral (PI) model following controller (MFC) is proposed. Then the model-following error is used to adaptively update the gains of the MFC via the affine projection algorithm (APA). The control strategy is very simple and easy to be implemented on digital signal processor (DSP). The validity and usefulness of the proposed control scheme are verified through experimental results.

Multiple-input dual-output adjustment scheme for semiconductor manufacturing processes using a dynamic dual-response approach

This paper presents an optimization-based, multiple-input dual-output (MIDO) run-to-run (R2R) controller for general semiconductor manufacturing processes. This controller, termed ‘adaptive dual-response optimizing controller’ (ADROC), can serve as a recipe regulator between consecutive runs of wafer fabrication. In ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual-response approach is used to seek the optimum recipe within the acceptability region for the execution of next run. In addition, a ‘responsive-type’ adjustment method is devised, serving as a post hoc filter to alleviate the over-control problem and maintain a better trade-off between two potentially conflicting process responses in MIDO R2R situations. Typical applications of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing are demonstrated through simulated processes to illustrate ADROC. The procedure is compared to three benchmark methods (OAQC, single- and double-EWMA controllers) in a simulation experiment.

Self-tuning of threshold for a two-state system

A two-state system (TSS) under time-periodic perturbations (to be regarded as input signals) is studied in connection with self-tuning (ST) of threshold and stochastic resonance (SR). By ST, we observe an improvement of the signal-to-noise ratio (SNR) in a weak-noise region. An analytic approach to a tuning equation reveals that SNR improvement is possible also for the large-noise region and this is demonstrated by Monte Carlo simulations of hopping processes in a TSS. ST and SR are discussed from a little more physical point of view of the energy transfer (dissipation) rate, which behaves in a similar way as the SNR. Finally ST is considered briefly for a double-well potential system, which is closely related to the TSS.

SISO run-to-run feedback controller using triple EWMA smoothing for semiconductor manufacturing processes

During the past decade, a variety of 'run-to-run' (R2R) control schemes have been proposed and extensively investigated under various assumed models of process dynamics and disturbances whilst applied to the setpoints (recipe) adjustment between batches for the production of semiconductor manufacturing. The R2R control mechanism of this kind represents an interdisciplinary methodology combining response surface modelling, engineering process control (EPC) and statistical process control (SPC), of which the main objective is to fine-tune the recipe so as to maintain the process output of each run as close to the nominal target as possible. In this paper, the single-input single-output (SISO) case is addressed. To overcome the shortcomings in published R2R controllers, an EWMA feedback controller through a strategy of triple smoothing is proposed (in line with the construction of EWMA statistics) which, in the standard R2R practice, compensates process shifts, a deterministic process drift, disturbance of coloured noises, as well as positive autocorrelation (up to the autoregressive order of two) present in the observed response. It is shown via an experimental study that the triple EWMA filter can provide better control performance for R2R applications as compared with those of the control actions of double EWMA and self-tuning (ST). At last, a relevant application to chemical mechanical planarization (CMP) in semiconductor manufacturing, a critical step in chip fabrication, is used to illustrate the proposed controller.

Influence of Self-Tuning Control Techniques on the Dynamics of a Synchronous Generator Unit

ABSTRACT The recent development In microprocessor technology makes Implementation of advanced control strategies feasible at the generating level. A self-tuning (ST) proportional-plus-lntegral-plus-derivative (PID) digital automatic voltage regulator (DAVR) for a large synchronous machine is proposed and the influence of this regulator on the generator dynamic and transient stability is investigated. The algorithm for this regulator combines a least-square estimator with a digital PID control algorithm. The parameters of the PID control algorithm are computed and updated according to the estimated model. The dynamic performance of the machine when equipped with a digital PID governor is also presented. A comparison of the computer results as obtained from the simulation study are compared with the available experimental results.

Self-Tuning Control of a Heat Conduction Process

The paper deals with a possible extension of the existing minimum variance (MV) and self-tuning (ST) control strategies for a class of distributed parameter systems (D.P.S.). The distributed output signal (D.O.S.) of the process is transformed to a single variable using the spatial weighted average (S.W.A.) transformation. If the process parameters are known it is easy to derive a minimum variance controller. In case of unknown process parameters the recursive least-squares technique is applied to estimate the parameters of the controller directly. The algorithm and its convergency properties are demonstrated by several computer simulations.

Selftuning control of some pilot plant processes

This paper reviews the results of some experimental tests carried out to evaluate the performance of selftuning (ST) controllers for temperature control of a continuous stirred tank heater, composition control of a binary distillation column and pH control of an acidic effluent. All the pilot plant units have been controlled using a single variant selftuning control, and a newly developed multivariant ST controller was used for simultaneous control of the terminal compositions of the distillation column. The control performance of the units operating under ST control is compared to that obtained using very well tuned proportional plus integral (PI) or proportional plus integral plus derivative (PID) conventional controllers. Control of pH is shown using a technique of electrochemical neutralisation coupled with a single variant ST controller. The control algorithms have been programmed on a number of microprocessor- and minicomputer-based systems. Z80 for stirred tank heater control, LS1-11 for pH control and HP1000 distributed computer system for distillation control.

Self-tuning adaptive control of cement raw material blending

A new, modified self-tuning (ST) minimum variance (MV) regulator algorithm developed for multiple input multiple output (MIMO) systems is presented with required average for finite time (RAFT). This control strategy has been developed for the self-tuning optimal control of cement raw material blending. After a brief survey of the technological background the algorithm of MIMO-ST-MV-RAFT control strategy is derived. A self-tuning blending control system is described with real-time experimental results illustrating the efficiency of the new multivariable regulator.

On Simultaneous Optimal Control of Raw Material Blending and a Ball Grinding Mill

In this paper an off-line computer control system developed for use in a cement raw material blending plant is described. A desk-top calculator is used to implement a new self-tuning (ST) minimum variance (MV) regulator algorithm developed for multiple-input, multiple-output (MIMO) systems. After a brief survey of technology the algorithm of the MIMO-ST-MV regulator is discussed. A description of the whole off-line computer control system follows. A program written in the PROCAL language is provided to illustrate computeraided design devices which facilitate the development of the software for the above-mentioned control tasks. The main steps in the preparation of a real-time software package for the raw material blending procedure as applicable to the present phase of the research are also considered.Starting from the material balance equations and the basic dynamic behaviour a new type of mill model has been constructed for control purposes. This new model describes the static and dynamic behaviour of the mill more exactly than previously published models. It contains conventional elements (integrator, summator, dead-time, nonlinearity) which can easily be simulated by computer techniques. The construction of the model follows the geometrical arrangement of the mill and thus its structure is clear and the effect of any modifications can easily be determined from the model.The model also produces both static and dynamic unstable phenomena. It is useful for designing extremum control equipment and in adjusting its parameters via simulation. Generally this model facilitates the design of conventional control loops of ball mills and the preparation of DDC circuits.

Self-Tuning Computer Control of Cement Raw Material Blending

In this paper an off-line (in-line) computer control system of a cement raw material blending plant by a desk-top calculator is considered using a new self-tuning (ST) minimum variance (MV) regulator algorithm developed for multiple input multiple out-put (MIMO) systems. After a brief survey of technology the algorithm of MIMO-ST-MV regulator is shortly discussed.Then the whole off-line (in-line) computer control system is described.A program written in PROCAL language is given to illustrate our computer aided design devices which make easy to develop the software of the 8bove control task.The main steps for making ready a real-time software package to the raw material blending are also considered at the present phase of the research work.