Automatic Tuning Procedure Research Articles

On-line automatic controller tuning of a multivariable grinding mill circuit using Bayesian optimisation

Process controllers are abundant in the industry and require attentive tuning to achieve optimal performance. While tuning controllers by the most primitive method of trial and error is possible, it often leads to sub-optimal performance if not conducted by a skilled expert. It is much more appealing to develop an on-line, sample efficient, automated tuner which can optimise the performance of a given controller to the task at hand. The automatic tuning procedure can be conducted during commissioning, when poor controller performance is observed or when process conditions have changed. The problem statement is formulated as the minimisation of an objective function constructed to achieve the desired controller performance. In this context the automatic tuning problem of multi-input multi-output (MIMO) controllers is considered within the framework of Bayesian optimisation and applied in simulation to an ore milling circuit with three manipulated and three controlled variables. Regulatory and set point tracking controllers are tuned automatically and are shown to achieve better performance than a reference controller.

Pacejka-Like Curve-Based Speed Reference Generators for Electric Vehicles Powered by in-Wheel Motors

Recently, motor speed reference generators have been designed for the cruise control of electric vehicles with either centralized electric motors (straight manoeuvres) or in-wheel motors (bend manoeuvres with sufficiently small constant steering angles). Steady-state operation at a safe (though conservative) tire longitudinal slip can be achieved, with no a priori knowledge regarding the occurrence of a specific external condition. Here, an innovative solution is presented. It is based on an ingenious use of Pacejka-like curves – representing the torque current-slip characteristics of the vehicle – to design a new contraction-mapping-based automatic tuning procedure for the longitudinal velocity of the vehicle. Such an innovative procedure overcomes the highly conservative nature of the previous approach in terms of unduly small longitudinal velocities (and yaw rates) under relatively favourable external conditions while guaranteeing a safe operating condition close to the maximum of the torque-slip characteristics with no knowledge of the tire-road adhesion coefficient. Comparative CarSim simulations illustrate the effectiveness of the proposed approach, in the presence of uncertain parameters and complex vehicle dynamics that are neglected at the control design stage.

A retrofitting control system design suitable for deep borehole drilling using legacy draw-works mechanical brake hardware

Deep drilling operations are typically needed when producing deep wells for hydrocarbon exploitation or geothermal energy applications. Since a notable portion of existing deep drilling rigs are still fielding manually operated draw-works (hoist) drum mechanical brake actuators, their usage puts notable constraints on the quality of borehole production and corresponding production rates. By retrofitting such mature drilling rigs with a suitable brake actuator servo mechanism, completely automatic drilling operation can be facilitated, thus also achieving borehole production efficiency improvements and meeting the increasingly stringent requirements on drilling control system performance. To this end, this paper presents the development of an automatic drilling control system based on an electrical servoactuator of the draw-works mechanical brake, with the control system arranged in the so-called cascade control system structure, and an automatic tuning procedure having been devised as well. Its robustness has been tested by means of root locus plots and simulation analysis of dominant closed-loop dynamics effects. The proposed control system has been verified by means of simulations first, which has been followed by a thorough experimental verification on an actual deep drilling rig retrofitted with electrical servodrive based-actuator of the winch drum mechanical brake. Field results have indicated that the proposed control system can maintain the drill-bit normal force within ± 10% of the target value during normal operation, with penetration rate variations typically being two to three times smaller compared to the case of human operator controlling the drilling process.

EmcFIS: Evolutionary multi-criteria Fuzzy Inference System for virtual network function placement and routing

With the increasing demands for low-delay network services, mobile edge computing (MEC) has emerged as an appealing solution to provide computing resources in close to the end users. Network function virtualization (NFV) is a new network architecture which replaces dedicated hardware middleboxes with software instances to run network functions via software virtualization on general-purpose servers deployed at edge clouds. Because of the resource limitation at network edges, efficient placement and routing for online virtual network function requests (VNF-PRO) is a challenging task. The VNF-PRO has proven to be NP-hard, and thus, metaheuristic algorithms are the best choice in term of the solution quality. However, metaheuristics suffer from high computational complexity, and cannot be performed for online requests in the VNF-PRO. In this paper, a combined model based on fuzzy logic and genetic algorithm is proposed to achieve proper solution quality-speed trade-off in the VNF-PRO. In this method, a multi-criteria fuzzy inference system (named mcFIS) is used for the online VNF placement and routing. To achieve the best performance, a multi-objective evolutionary algorithm based on genetic algorithm (GA) is utilized in an offline procedure for automatic rule tuning of the mcFIS, once before applying it for online applications. Simulation results on two NFV benchmark instances demonstrate the efficiency of the proposed model against the existing techniques.

Design Criteria for Flux-Weakening Control Bandwidth and Voltage Margin in IPMSM Drives Considering Transient Conditions

Flux-weakening (F-W) methods based on feedback voltage regulation are widely adopted in interior permanent magnet synchronous motor (IPMSM) drives. Generally, the setpoint must be lower than maximum available voltage, to prevent saturation of current regulators during transients. However, the margin should be small, to exploit torque capability of the machine and avoid additional current magnitude during F-W operation. The trade-off between F-W control bandwidth and the necessity of voltage margin has not been analyzed in-depth in previous literature. In fact, F-W regulator design aiming at a certain bandwidth has been addressed by means of gain adaptation, but the choice of the target bandwidth remained as an open issue. In this article, the crucial transient conditions which can cause the saturation of current regulators, specifically the reduction of input voltage, the fast acceleration of the drive and the transient torque demand, are considered. The input voltage reduction affects the available voltage, while fast acceleration and increase of torque demand push towards higher voltage, requiring disturbance rejection action by the F-W controller. All these conditions are analyzed in terms of worst-case requirement and the proposed analytical modeling is verified by means of simulations and experiments. The findings can be applied in the design of automatic tuning procedures and design for high-reliability or safety-critical applications.

Automatic Delay Tuning of a Novel Ring Resonator-Based Photonic Beamformer for a Transmit Phased Array Antenna

We present a novel photonic beamformer for a fully integrated transmit phased array antenna, together with an automatic procedure for tuning the delays in this system. Such an automatic tuning procedure is required because the large number of actuators makes manual tuning practically impossible. The antenna system is designed for the purpose of broadband aircraft-satellite communication in the Ku-band to provide satellite Internet connections on board the aircraft. The goal of the beamformer is to automatically steer the transmit antenna electronically in the direction of the satellite. This is done using a mix of phase shifters and tunable optical delay lines, which are all integrated on a chip and laid out in a tree structure. The Ku-band has a bandwidth of 0.5 GHz. We show how an optical delay line is automatically configured over this bandwidth, providing a delay of approximately 0.4 ns. The tuning algorithm calculates the best actuator voltages based on past measurements. This is the first time that such an automatic tuning scheme is used on a photonic beamformer for this type of transmit phased array antenna. We show that the proposed method is able to provide accurate beamforming (< 11.25{\deg} phase error over the whole bandwidth) for two different delay settings.

Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection.

This paper presents an automatic parameter tuning procedure specially developed for a dynamic adaptive thresholding algorithm for fruit detection. One of the major algorithm strengths is its high detection performances using a small set of training images. The algorithm enables robust detection in highly-variable lighting conditions. The image is dynamically split into variably-sized regions, where each region has approximately homogeneous lighting conditions. Nine thresholds were selected to accommodate three different illumination levels for three different dimensions in four color spaces: RGB, HSI, LAB, and NDI. Each color space uses a different method to represent a pixel in an image: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), LAB (Lightness, Green to Red and Blue to Yellow) and NDI (Normalized Difference Index, which represents the normal difference between the RGB color dimensions). The thresholds were selected by quantifying the required relation between the true positive rate and false positive rate. A tuning process was developed to determine the best fit values of the algorithm parameters to enable easy adaption to different kinds of fruits (shapes, colors) and environments (illumination conditions). Extensive analyses were conducted on three different databases acquired in natural growing conditions: red apples (nine images with 113 apples), green grape clusters (129 images with 1078 grape clusters), and yellow peppers (30 images with 73 peppers). These databases are provided as part of this paper for future developments. The algorithm was evaluated using cross-validation with 70% images for training and 30% images for testing. The algorithm successfully detected apples and peppers in variable lighting conditions resulting with an F-score of 93.17% and 99.31% respectively. Results show the importance of the tuning process for the generalization of the algorithm to different kinds of fruits and environments. In addition, this research revealed the importance of evaluating different color spaces since for each kind of fruit, a different color space might be superior over the others. The LAB color space is most robust to noise. The algorithm is robust to changes in the threshold learned by the training process and to noise effects in images.

Direct Torque Control of Induction Motor by Using Particle Swarm Optimization Technique

This paper is based on an efficient and reliable evolutionary approach of particle swarm optimization (PSO) using direct torque control (DTC) of induction motor. In order to resolve the problem of parameter variation the PI controllers are generally used in industrial plants because it is uncomplicated and robust. However, there is a problem in changing PI parameters. So, the engineers are looking for automatic tuning procedures. In traditional direct torque-controlled induction motor drive, there is generally undesired torque and ripple in form of flux. So Tuning PI parameters (Kp, Ki) are critical to DTC system to improve the performance of the system. In this paper, particle swarm optimization (PSO) is planned to correct the parameters (Kp, Ki) of the speed controller in order to get improved performance of the system and also responsible to run the machine at base speed.

The one-third rule for PI controller tuning

Despite academic advances in PID controller design and development of automatic tuning procedures, there are still many PID controllers that are badly tuned, or not even tuned at all, in process industry. There are several reasons for this, but one major reason is that it is considered too time consuming to use current PID controller tuning methods. This paper presents a new tuning method that is significantly simpler and faster to use compared to the methods that form the current industry standard, but still gives tuning parameters that are fairly close to those obtained by the more advanced methods.

Automatic EKF Tuning for UAS Path Following in Turbulent Air

By using two simultaneously working Extended Kalman Filters, a procedure is implemented in order to perform in a fully autonomous way the path following in turbulent air. To guarantee the robustness of the proposed algorithm, an automatic tuning procedure is proposed to determine optimal values of Process and Measurement Noise statistics. Such a procedure is based on both the characteristics of the disturbances and the desired flight path; in particular, a specific performance index is applied to tune filters. In this way control laws are adapted to the flight condition and these lead to an optimal path-following. This research represents an upload of previous papers. It allows eliminating the time expensive trial and error procedure usually employed to tune Extended Kalman Filters. Obviously, procedure results are optimal for ever flight condition.

Iterative tuning of modified uncertainty and disturbance estimator for time-delay processes: A data-driven approach

Uncertainty and disturbance widely exist in the process industry, which may deteriorate control performance if not well handled. The uncertainty and disturbance estimator (UDE) emerges as a promising solution by treating the external disturbances and internal uncertainties simultaneously as a lumped term. To overcome its limitation caused by time delay, a modified UDE (MUDE) has been proposed recently. However, its parameter tuning relies heavily on trial-and-error, thus being time-consuming in balancing the robustness and performance. To this end, this paper aims to develop an automatic tuning procedure for the MUDE-based control system. The quantitative relationship between system performance and the scaled parameters is empirically built. Iterative Feedback Tuning (IFT) is utilized to approximate the nominal model towards actual process. Through the empirical formula and optimized model, an automatic design procedure is proposed after taking into account the system robustness and output performance simultaneously. Simulation results show the superiority of the closed-loop performance over the original MUDE controllers. The experimental results validate the feasibility of the method proposed in this paper, depicting a promising prospect in the practical application.

Autonomous Vehicle Control Through the Dynamics and Controller Learning

Parameters tuning of the model and the controller is an essential problem for autonomous vehicles. Traditionally, parameter tuning work is accomplished by manual operation or grid search, which is a tedious and time-consuming work. Recently, thanks to the development of machine learning community, several automatic controller parameter tuning approaches emerged, which usually model the performance function as a Gaussian process, and complete the automatic tuning procedure via Bayesian optimization. However, the existing approaches rarely consider the time-varying feature of the system performance, which is practical in many scenarios, induced by the unmodeled interactions between the system and the environment. In this paper, we take both of the dynamic model uncertainty and the controller parameters uncertainty into account, and tune them to find a global optimal choice for minimizing the time-varying control costs, which is modeled by the time-varying Gaussian process. We provide a novel algorithm, named as time-varying controller optimization. We validate our approach on synthetic simulation and real experiment, respectively. Taking the cumulative regret as the performance metric, we find that our approach has a better performance compared with the stationary algorithm.

HCC‐based interleaved boost converter with optimal switching frequency control of wind energy conversion system for DC microgrid application

This study exploited the hysteresis current control (HCC) with optimal switching frequency for interleaved boost converter to improve the power factor of wind energy conversion system (WECS) exclusively for DC microgrid applications. By fact that output power from WECS is variable due to unpredictable wind speed, where the HCC suffers with range of switching frequency, high switching loss, large input current ripple and poor power factor. To overcome the aforementioned problem, an automatic tuning procedure has been developed for proportional–integral–derivative (PID) voltage regulator to achieve optimal feedback gain of filter inductor current and thereby optimal switching frequency is maintained with good power factor, low harmonic distortion and improved efficiency. Additionally, the robust PID voltage regulator guarantees the stability and investigated through bode plots. Hardware prototype model is implemented in the laboratory standards with Spartan 3AN field programmable gate array control board. The experimental results obtained are confirming the theoretical background aspects of the operating regimes.

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

Abstract. In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function, principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low-resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

Implementation of PID autotuning procedure in PLC controller

In this paper, we present the automatic PID tuning procedure based on the Method of Moments and AMIGO tuning rules. The advantage of the Method of Moments is that the time constant and transport delay are estimated at the areas rather than on the individual points. This results in high resistance to the measurement noises. The sensitivity to measurement noises is a serious problem in other autotuning methods. The second advantage of this method is that it approximates plant during identification process to first order model with time delay. We combined the Method of Moments with the AMIGO tuning rules and implemented this combination as a stand-alone autotuning procedure in Siemens S7-1200 PLC controller. Next, we compared this method with two built-in PID autotuning procedures which were available in Siemens S7-1200 PLC controller. The procedure was tested for three types of plant models: with lag-dominated, balanced, and delay-dominated dynamics. We simulated the plants on a PC in Matlab R2013a. The connection between the PC and PLC was maintained through a National Instruments data acquisition board, NI PCI-6229. We conducted tests for step change in the set point, trajectory tracking, and load disturbances. To assess control quality, we used IAE index. We limited our research to PI algorithm. The results prove that proposed method was better than two built-in tuning methods provided by Siemens, oscillating between a few and even a dozen percent in most cases. The proposed method is universal and can be implemented in any PLC controller.

Pre-optimization of Asymmetrical Underplatform Dampers

The numerical coupled optimization of an underplatform damper is the exploration of its dynamics through a finite element model which includes both the damper and the blades. This is an effective approach if the initial damper mass and geometry have been previously selected (i.e., pre-optimized) in such a way that those parameter combinations leading to undesirable damper behavior are ruled out a priori: —ensure that damper jamming is avoided by ruling out the undesirable combinations of platform and friction angles; —ensure that damper lift-off is avoided through an appropriate choice of the shape and position of the damper-platform flat contact surface and the position of the damper mass center; —set upper and lower to the value of damper-platform contact forces (as a multiple of the damper centrifugal force), the first being related to friction and wear problems, and the second to the very existence of bilateral contacts. The above is strongly dependent on the effective values of friction coefficients, which can vary by a factor of over two with temperature, frequency and contact pressure. The paper illustrates the pre-optimization procedure using, as an example, a rigid bar damper with a curved-flat cross section. In order to validate the method against experimental data and to determine the necessary real contact parameters, the paper capitalizes on already developed tools presented in the previous ASME papers: the test rig developed at the AERMEC lab, the numerical model representing the damper dynamics, and the automatic random sampling tuning procedure.

Design of self-tuneable mass damper for modular fixturing systems

Enhancing dynamic characteristics of fixtures for large workpieces is essential to assure chatter free machining of heavy-duty milling operations. Variable stiffness tuned mass dampers (VSTMD) can effectively improve the dynamic stiffness of modular fixtures by changing their dynamic characteristics. The theory of a new VSTMD concept is presented. Their realisable optimal tuning is determined and the results are compared to the standard constant stiffness TMDs. By means of the developed automatic tuning procedure, stiffness is varied via a rotary spring, while damping is provided by eddy currents. The prototype and the effectiveness of the concept are experimentally validated by heavy-duty milling tests on a modular fixture.

Self-Tuning Mechanism for the Design of Adaptive Secondary Mirror Position Control

Deformable mirrors (DMs) are electromechanical devices used in ground-based telescopes to compensate for the distortions caused by the atmospheric turbulence, the main factor limiting the resolution of astronomical imaging. Adaptive secondary mirrors (ASMs) represent a new type of DMs; two of them have been recently installed on the 8-m-class large binocular telescope (LBT). ASMs are able to jointly correct rigid and nonrigid wave-front distortions thanks to the use of force actuators distributed on the overall mirror surface. As an offset, each actuator needs to be piloted by a dedicated controller, whose parameters must be accurately tuned to obtain the desired mirror shape. At the present time, the calibration of the controller parameters is executed manually. This paper presents a novel automatic controller tuning procedure that does not rely on the modeling of the mirror dynamics. The experimental validation on a prototype reproducing the three innermost rings of the LBT ASM is reported.

Optimum Induction Motor Speed Control Technique Using Genetic Algorithm

Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. In order to overcome this problem of parameter variation the PI controllers are widely used in industrial plants because it is simple and robust. However there is a problem in tuning PI parameters. So the control engineers are on look for automatic tuning procedures. In recent years, many intelligence algorithms are proposed to tuning the PI parameters. Tuning PI parameters using different optimal algorithms such as the simulated annealing, genetic algorithm, and particle swarm optimization algorithm. In this paper a scheduling PI tuning parameters using genetic algorithm strategy for an induction motor speed control is proposed. The results of our work have showed a very low transient response and a non-oscillating steady state response with excellent stabilization. The simulation results presented in this paper show the effectiveness of the proposed method, with satisfied response for GA-PI controller.

Optimum Induction Motor Speed Control Technique Using Particle Swarm Optimization

Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. In order to overcome this problem of parameter variation the PI controllers are widely used in industrial plants because it is simple and robust. However there is a problem in tuning PI parameters. So the control engineers are on look for automatic tuning procedures. In recent years, many intelligence algorithms are proposed to tuning the PI parameters. Tuning PI parameters using different optimal algorithms such as the simulated annealing, genetic algorithm, and particle swarm optimization algorithm. In this paper a scheduling PI tuning parameters using particle swarm optimization strategy for an induction motor speed control is proposed. The results of our work have showed a very low transient response and a non-oscillating steady state response with excellent stabilization. The simulation results presented in this paper show the effectiveness of the proposed method, with satisfied response for PSO-PI controller.