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Öğe 2DOF multi-objective optimal tuning of disturbance reject fractional order PIDA controllers according to improved consensus oriented random search method(Elsevier, 2020) Ozbey, Necati; Yeroglu, Celaleddin; Alagoz, Baris Baykant; Herencsar, Norbert; Kartci, Aslihan; Sotner, RomanThis study presents a Fractional Order Proportional Integral Derivative Acceleration (FOPIDA) controller design methodology to improve set point and disturbance reject control performance. The proposed controller tuning method performs a multi-objective optimal fine-tuning strategy that implements a Consensus Oriented Random Search (CORS) algorithm to evaluate transient simulation results of a set point filter type Two Degree of Freedom (2DOF) FOPIDA control system. Contributions of this study have three folds: Firstly, it addresses tuning problem of FOPIDA controllers for first order time delay systems. Secondly, the study aims fine-tuning of 2DOF FOPIDA control structure for improved set point and disturbance rejection control according to transient simulations of implementation models. This enhances practical performance of theoretical tuning method according to implementation requirements. Thirdly, the paper presents a hybrid controller tuning methodology that increases effectiveness of the CORS algorithm by using stabilizing controller coefficients as an initial configuration. Accordingly, the CORS algorithm performs the fine-tuning of 2DOF FOPIDA controllers to achieve an improved set point and disturbance rejection control performances. This fine-tuning is carried out by considering transient simulation results of 2DOF FOPIDA controller implementation model. Moreover, Reference to Disturbance Ratio (RDR) formulation of the FOPIDA controller is derived and used for measurement of disturbance rejection control performance. Illustrative design examples are presented to demonstrate effectiveness of the proposed method. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Cairo University.Öğe Adaptive Control of Nonlinear TRMS Model by Using Gradient Descent Optimizers(Ieee, 2018) Alagoz, Baris Baykant; Tepljakov, Aleksei; Kavuran, Gurkan; Alisoy, HafizThis study demonstrates an application of direct gradient descent control for adaptively control of a nonlinear stable system models. The approach is based on utilization of gradient descent optimization techniques for the synthesis of control signals to control a specific plant model. In a former work, gradient descent optimizers were designed by considering a first degree instant input-output relation model assumption of the controlled system and this can allow model independent adaptive control of a class of plant models that can approximate to first order stable plant dynamics. The current study is an extension of this scheme for the purpose of nonlinear adaptive control. Here, we consider a higher degree polynomial assumption of instant input-output relations of the controlled system to obtain gradient descent optimizers that can be applied for adaptive control of a class of nonlinear systems. For evaluation of control performance of gradient descent optimizers, it is applied for the control of nonlinear TRMS model and the results are compared with performance of conventional PID control.Öğe Adaptive Gradient Descent Control of Stable, First Order, Time-delay Dynamic Systems According to Time-Varying FIR Filter Model Assumption(Ieee, 2019) Yagmur, Nagihan; Alagoz, Baris BaykantThis study investigates robust control performance of adaptive gradient descent control in case of parametric perturbation of first order stable LTI systems. The proposed adaptive gradient descent control method is a variant of direct gradient descent control. The study aims to implement an adaptive control scheme for modeling-free control of stable, first-order, time delay plant models. The method implements two gradient descent optimizers. The first one performs only for synthesis of control signal, and the second optimizer works for a short-time model prediction based on instant input-output relation of a plant. We use a time-varying finite impulse response (TV-FIR) form to approximate short-term input-output relations of a first order stable plant dynamics and this work is an extended version of adaptive gradient descent control schemes that were presented in [6] and [7]. Adaptation and control laws are derived for this FIR model premise according to gradient descent method. The robust control performance of the proposed control method is investigated according to simulation results and compared with performance of optimal PI controller designs.Öğe Auto-tuning of PID controller according to fractional-order reference model approximation for DC rotor control(Pergamon-Elsevier Science Ltd, 2013) Alagoz, Baris Baykant; Ates, Abdullah; Yeroglu, CelaleddinThis paper presents a stochastic, multi-parameters, divergence optimization method for the auto-tuning of proportional-integral-derivative (PID) controllers according to a fractional-order reference model. The study aimed to approximate the step response of the real closed-loop flight control system to the response of a theoretical reference model for a smoother and more precise flight control experience. The proposed heuristic optimization method can auto-tune a PID controller without a precise plant model. This is very advantageous when dealing with model and parameter uncertainties in real control application and practice. Experimental study confirms the reference model driven auto-tuning of the DC rotor prototype. (C) 2013 Elsevier Ltd. All rights reserved.Öğe Behavioural modelling of delayed imbalance dynamics in nature: a parametric modelling for simulation of delayed instability dynamics(Taylor & Francis Ltd, 2022) Alagoz, Baris Baykant; Deniz, Furkan Nur; Koseoglu, MuratImbalance dynamics can develop very slowly, and real systems and structures may seem to be stable or balanced for long periods of time before signs of instability behaviour become apparent. This study presents two dynamic system modelling approaches for simulation of delayed instability: Firstly, frequency domain properties of the system instability are investigated, and a parametric model to represent delayed instability behaviour is formulated according to the system pole placement technique. Secondly, a new type of instability modelling approach, which is based on time-domain characteristics of fractional order derivative operators, is introduced by utilizing the finite convergence regions of the Binomial series. This special instability modelling technique essentially uses the region of convergence in the series expansion of impulse responses. Several illustrative modelling and simulation examples are illustrated for engineering problems such as slowly developing cracks in metals, the voltage collapse in power systems and the delayed instability in control systems.Öğe Comparision of Solutions of Numerical Gradient Descent Method and Continous Time Gradient Descent Dynamics and Lyapunov Stability(Ieee, 2019) Yagmur, Nagihan; Alagoz, Baris BaykantGradient descent dynamics is an optimization techniques that is widely used in machine learning applications. This technique updates model parameter in the direction of descending of learning error. In this study, Lyapunov stability of continuous time gradient descent dynamics is investigated and robust stability condition, which is needed for implementation of gradient descent dynamics in intelligent control system applications, is evaluated. In a illustrative example, for a De Jong's function type error function, solutions of continuous gradient descent dynamics and Euler method based numerical solutions are compared and stability concerns is discussed.Öğe DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction(Springer, 2023) Ari, Davut; Alagoz, Baris BaykantStock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87% average accuracy in buy-sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8% more average income compared to the average income of a long-term investment strategy.Öğe Design of Fractional-Order PI Controllers for Disturbance Rejection Using RDR Measure(Ieee, 2014) Deniz, Furkan Nur; Alagoz, Baris Baykant; Keles, Cemal; Tan, NusretParameter uncertainties and unpredictable environmental disturbances reduce control performance of real control systems. For a robust control performance, stability and disturbance rejection are two main concerns that should be addressed in practical controller design problems. This paper presents an analysis to deal with system stability and disturbance rejection control for fractional-order PI (FOPI) controllers. Stability Boundary Locus (SBL) is calculated for an example with FOPI control system and Reference to Disturbance Rate (RDR) performance is investigated for the chosen stable FOPI designs from the stability region obtained using SBL. MATLAB/Simulink simulation examples are used to demonstrate stable and Disturbance Rejection Control (DRC) of FOPI control systems and presents comparisons for various designs of FOPI controllers.Öğe Design of Robust PI Controllers for Interval Plants With Worst-Case Gain and Phase Margin Specifications in Presence of Multiple Crossover Frequencies(Ieee-Inst Electrical Electronics Engineers Inc, 2022) Matusu, Radek; Senol, Bilal; Alagoz, Baris Baykant; Pekar, LiborThis article deals with the computation of robustly performing Proportional-Integral (PI) controllers for interval plants, where the performance measures are represented by the worst-case Gain Margin (GM) and Phase Margin (PM) specifications, in the event of multiple Phase Crossover Frequencies (PCFs) and/or Gain Crossover Frequencies (GCFs). The multiplicity of PCFs and GCFs poses a considerable complication in frequency-domain control design methods. The paper is a continuation of the authors' previous work that applied the robust PI controller design approach to a Continuous Stirred Tank Reactor (CSTR). This preceding application represented the system with a single PCF and a single GCF, but the current article focuses on a case of multiple PCFs and GCFs. The determination of a robust performance region in the P-I plane is based on the stability/performance boundary locus method and the sixteen plant theorem. In the illustrative example, a robust performance region is obtained for an experimental oblique wing aircraft that is mathematically modeled as the unstable interval plant. The direct application of the method results in the (pseudo-)GM and (pseudo-)PM regions that illogically protrude from the stability region. Consequently, a deeper analysis of the selected points in the P-I plane shows that the calculated GM and PM boundary loci are related to the numerically correct values, but that the results may be misleading, especially for the loci outside the stability region, due to the multiplicity of the PCFs and GCFs. Nevertheless, the example eventually shows that the important parts of the GM and PM regions, i.e., the parts that have an impact on the final robust performance region, are valid. Thus, the method is applicable even to unstable interval plants and to the control loops with multiple PCFs and GCFs.Öğe Detection of RR Interval Alterations in ECG Signals by Using First Order Fractional Filter(Ieee, 2016) Alagoz, Baris Baykant; Yeroglu, Celaleddin; Kavuran, Gurkan; Ates, AbdullahSleep apnea syndrome deteriorates sleeping quality and daily performance of many individuals. This study presents utilization of fractional-order low pass filtering for the detection of RR interval alteration from electrocardiogram (ECG) signals. In the case of sleeping apnea, cardiac interbeat intervals prolong and it is viewed as an indication of obstructive sleep apnea state. The prolonged interbeat intervals manifest themselves as the decrease of R peak frequency (increase of RR intervals) in ECG signals. It results in shifting of spectral components composing R peaks towards lower frequencies in energy signal of ECG. In order to detect prolonging interbeat intervals, energy signal calculated from ECG is applied to low-pass fractional-order filter. Transition band of the low-pass filter is used as a ramp filter in order to detect frequency variations in the energy of R peaks. We compare results of the first order fractional-order and integer order low-pass filters and demonstrate that the fractional-order filter can improve the detection performance of low-pass filtering by modifying transition band slope of low-pass filters.Öğe A differential evolutionary chromosomal gene expression programming technique for electronic nose applications(Elsevier, 2023) Ari, Davut; Alagoz, Baris BaykantThe intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.(c) 2023 Elsevier B.V. All rights reserved.Öğe Discretization of Fractional Order Transfer Functions by Weighted Multi-Objective Particle Swarm Optimization Method(Ieee, 2017) Imik, Ozlem; Alagoz, Baris BaykantIn order to implement fractional order transfer function (FOTF) model in digital systems, transfer function discretization methods are developed to obtain discrete filters that provide acceptable amplitude or phase response approximations to FOTFs in operating frequency ranges. This paper presents an approach to improve discrete IIR filter approximations to FOTFs according to application requirements by using Particle Swarm Optimization (PSO). For this purpose, particles of PSO are initialized around the solution of a well-known analytical approximation method and then, these particles search stable IIR filter solutions in the filter design space to improve magnitude and phase response approximation performance over a desired frequency region. Specifically, PSO algorithm is used to fine-tune results of Tustin recursive discretization method according to a weighed multi-objective cost function. This cost function is expressed as the weighted sum of magnitude and phase response matching objectives with a frequency weighting. The frequency weighting is employed for prioritization of frequency regions in optimization. The proposed method also ensures the stability of optimized IIR filter approximations by enforcing particles to move into stable filter solution regions of the search space. This design approach can contribute to the digital realization of fractional order systems for practical applications.Öğe A distance-based dynamical transition analysis of time series signals and application to biological systems(Springer, 2012) Alagoz, Serkan; Alagoz, Baris BaykantThis study demonstrates an application of distance-based numerical measures to the phase space of time series signals, in order to obtain a temporal analysis of complex dynamical systems. This method is capable of detecting alterations appearing in the characters of the deterministic dynamical systems and provides a simple tool for the real-time analysis of time series data obtained from a complex dynamical system even with black box functionality. The study presents a possible application of the method in the dynamical transition analysis of real EEG records from epilepsy patients.Öğe DISTURBANCE REJECTION FOPID CONTROL OF ROTOR BY MULTI-OBJECTIVE BB-BC OPTIMIZATION ALGORITHM(Amer Soc Mechanical Engineers, 2017) Ates, Abdullah; Alagoz, Baris Baykant; Yeroglu, Celaleddin; Yuan, Jie; Chen, YangQuanThis paper presents a FOPID tuning method for disturbance reject control by using multi-objective BB-BC optimization algorithm. Proposed method allows multi-objective optimization of set-point performance and disturbance rejection performances of FOPID control system. The objective function to be minimized is composed of the weighted sum of MSE for set-point performance and RDR for disturbance rejection improvement. The proposed optimization performs maximization of RDR and minimization of MSE and it can deal with the tradeoff between RDR performance and step-point performance. Application of the method is shown for auto tuning of FOPID controller that is employed for control of TRMS model. We observed that low-frequency RDR indices can be used to improve disturbance rejection performance in multi-objective controller tuning problems. Particularly, for flight control application, disturbance reject control is very substantial to robust performance of propulsion systems.Öğe Disturbance rejection FOPID controller design in v-domain(Elsevier, 2020) Tufenkci, Sevilay; Senol, Bilal; Alagoz, Baris Baykant; Matusu, RadekDue to the adverse effects of unpredictable environmental disturbances on real control systems, robustness of control performance becomes a substantial asset for control system design. This study introduces a v-domain optimal design scheme for Fractional Order Proportional-Integral-Derivative (FOPID) controllers with adoption of Genetic Algorithm (GA) optimization. The proposed design scheme performs placement of system pole with minimum angle to the first Riemann sheet in order to obtain improved disturbance rejection control performance. In this manner, optimal placement of the minimum angle system pole is conducted by fulfilling a predefined reference to disturbance rate (RDR) design specification. For a computer-aided solution of this optimal design problem, a multi-objective controller design strategy is presented by adopting GA. Illustrative design examples are demonstrated to evaluate performance of designed FOPID controllers. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Cairo University.Öğe Disturbance Rejection Fractional Order PID Controller Design in v-domain by Particle Swarm Optimization(Ieee, 2019) Tufenkci, Sevilay; Senol, Bilal; Alagoz, Baris BaykantDesign and stabilization problems of fractional order PID (FOPID) controllers have been generally solved in frequency, time and s-domains. This study presents a design scheme in v-domain for optimal disturbance reject FOPID controller tuning problem. The proposed method is based on optimally placement of minimum angle system poles inside stability region of the first Riemann sheet to improve disturbance rejection control performance. For a given stabilizing target angle of minimum angle system pole, the purposed design approach maximizes reference to disturbance rate (RDR) index. For this purpose, optimization problem is defined as maximization of RDR index subject to minimum angle pole placement constraint. This constraint ensures stability of resulting FOPID control system by placing the minimum angle system pole into stability region of v-domain. Particle swarm optimization (PSO) is implemented to solve this optimization problem. An illustrative design example is presented to show effectiveness of the proposed design method.Öğe An effective analog circuit design of approximate fractional-order derivative models of M-SBL fitting method(Elsevier - Division Reed Elsevier India Pvt Ltd, 2022) Koseoglu, Murat; Deniz, Furkan Nur; Alagoz, Baris Baykant; Alisoy, HafizThere is a growing interest in fractional calculus and Fractional Order (FO) system modeling in many fields of science and engineering. Utilization of FO models in real-world applications requires practical realization of FO elements. This study performs an analog circuit realization of approximate FO derivative models based on Modified Stability Boundary Locus (M-SBL) fitting method. This study demonstrates a low-cost and accurate analog circuit implementation of M-SBL fitting based approximate model of FO derivative elements for industrial electronics. For this purpose, a 4th order approximate derivative transfer function model of the M-SBL method is decomposed into the sum of first order low-pass filters form by using Partial Fraction Expansion (PFE) method, and the analog circuit design of the approximate FO derivative model is performed. Firstly, by using the final value theorem, authors theoretically show that the time response of the sum of first order low-pass filter form can converge to the time response of fractional order derivative operators. Then, the approximation performance of proposed FO derivative circuit design is validated for various input waveforms such as sinusoidal, square and sawtooth waveforms via Multisim simulations. Results indicate an accurate realization of the FO derivative in time response (an RMSE of 0.0241). The derivative circuit realization of the M-SBL fitting model in the form of the sum of first order low pass filters can yield a better time response approximation performance compared to the Continued Fraction Expansion (CFE) based ladder network realization of the approximate derivative circuit.Öğe An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application(Springer London Ltd, 2022) Ari, Davut; Alagoz, Baris BaykantAir quality control requires real-time monitoring of pollutant concentration distributions in large urban areas. Estimation models are used for the soft-calibration of low-cost multisensor data to improve precision of pollutant concentration measurements. This study introduces an integrated genetic programming dynamic neural network model for more accurate estimation of carbon monoxide and nitrogen dioxide pollutant concentrations from the multisensor measurement data. This model combines a genetic programming-based estimation model with a neural estimator model and improves estimation performances. In this structure, a genetic programming-based polynomial model works as a former estimator and it feeds the neural estimator model via a short-term former estimation memory. Then, the neural model utilizes this former estimation memory in order to enhance pollutant concentration estimations. This integration approach benefits from the correlation enrichment strategy that is performed by the former model. The proposed two-stage training procedure facilitates the training of the integrated models. In experimental study, the standalone genetic programming model, artificial neural network model, and the proposed integrated model are implemented to estimate carbon monoxide and nitrogen dioxide pollutant concentrations from the experimental multisensor air quality data. Results demonstrate that the proposed integrated model can decrease mean relative error about 10% compared to the standalone artificial neural network and about 28% compared to the standalone genetic programming estimation models. Authors suggested that the integrated estimation model can be used for more accurate soft-calibration of multisensor electronic noses in a wide-area air-quality monitoring application.Öğe An efficient PID-based optimizer loop and its application in De Jong's functions minimization and quadratic regression problems(Elsevier, 2022) Alagoz, Baris Baykant; Deniz, Furkan Nur; Koseoglu, MuratThe Proportional-Integral-Derivative (PID) control law has been commonly used for process control in control engineering applications. However, it has potential to work as a solver in optimization problems. This study introduces a PID-based optimizer loop that is designed to solve nonlinear, unconstrained, multi-parameter optimization problems. To achieve the minimization of multi-parameter positive real objective functions by using a closed loop PID control loop, a slope sentient objective function model is suggested to allow zero-crossing of the error signal. Thus, this objective function model enhances the convergence efficiency of the PID-based optimizer loop by indicating slope direction of the objective function and operating in both positive and negative error regions. The boundedness and convergence theorems for the proposed PID optimizer loop are presented, and a theoretical background for the PID-based minimization is established. To demonstrate practical minimization performance, numerical applications of the proposed PID optimizer loops are illustrated in the solution of two fundamental optimization problems. These are the minimization of 30 parameters De Jong's functions and the solution of quadratic regression problems. Also, an experimental study is presented for the quadratic regression modeling of measurement data from a hole-drilling experiment. Optimization results reveal that the proposed PID-based optimizer system can improve convergence speed and accuracy compared to performances of fundamental nonlinear optimization techniques. (c) 2021 Elsevier B.V. All rights reserved.Öğe Electronically reconfigurable two-path fractional-order PI/D controller employing constant phase blocks based on bilinear segments using CMOS modified current differencing unit(Elsevier Sci Ltd, 2019) Sotner, Roman; Jerabek, Jan; Kartci, Aslihan; Domansky, Ondrej; Herencsar, Norbert; Kledrowetz, Vilem; Alagoz, Baris BaykantThis work introduces a versatile type of electronically controllable bilinear transfer segments, i.e. two ports allowing independent electronic control of zero and pole of transfer function, based on modified current differencing unit (MCDU) active element. These proposed bilinear transfer segments serve for construction of fractional-order constant phase block (phi(alpha) = +/- 15 degrees and +/- 36 degrees, i.e. orders alpha = +/- 1/6 and +/- 2/5, tested in our case) representing electronically controllable integrator or differentiator (in dependence on current demand) as a part of novel two-path system of the fractional-order proportional-integral or derivative (FOPI/D) controller. The example of design procedure employs four bilinear transfer segments, electronically controllable proportional path and summing stage. Cadence IC6 Spectre simulation results (TSMC 0.18 mu m CMOS process) in both frequency and time domain are performed in order to confirm expected behavior of the system. An application of the proposed FOPI controller in control of linear voltage regulator is also demonstrated and performance improvements of the proposed design are discussed.
