Input Scaling Factors Research Articles

Control of quadrotor robot via optimized nonlinear type-2 fuzzy fractional PID with fractional filter: Theory and experiment

This paper presents a novel approach for controlling quadrotor robots. It introduces an optimized nonlinear fractional order type-2 fuzzy system integrated with a Fractional Order Proportional-Integral-Derivative controller, supplemented by a fractional filter in the derivative section. The input scaling factors, which represent the scaling coefficients associated with the input variables of the fuzzy system, are adjusted using nonlinear gains. The proposed controller combines type-2 fuzzy logic, fractional calculus, and nonlinear gains to enhance the control performance of the system. Its purpose is to provide superior control accuracy, robustness, and disturbance rejection capabilities. In order to enhance the performance of the quadrotor, the Bat Optimization Algorithm is utilized to finely adjust the controller parameters and membership function parameters of the fuzzy system. The fuzzy system incorporates Gaussian membership functions and applies the Mamdani min-max method for inference, utilizing the centroid method for defuzzification. The performance of the controller is evaluated in two scenarios: tracking aerial maneuvers in the presence of wind disturbance, and achieving angular stabilization by tracking a pulse function in the Hardware-In-The-Loop real-time test. The findings demonstrate significant improvements compared to traditional PID controllers. These improvements surpass 34 % without a filter and 53 % with a filter, in all directions and scenarios.

An echo state network model with the protein structure for time series prediction

From the perspective of bionics of biological structure, this paper proposes a new reservoir topology structure with an α-helix form of the secondary protein, named S-ESN. This network model has some advantages compared with the standard leaky-echo state network (Leaky-ESN) model. Because the neurons in the traditional reservoir are randomly and sparsely connected, the stability of the echo state network (ESN) will be reduced, and the prediction accuracy will also be decreased. The S-ESN model proposed greatly improves the internal stability of the reservoir, the dynamic activity of neurons and the prediction accuracy of the ESN. At the same time, the improved moth-flame optimization algorithm (MFO) with the probability of jump disturbance is used to optimize the three parameters: the leakage rate (a), the spectral radius (ρ), and the input scaling factor (sin), which can further improve the stability and predictability of the S-ESN. In order to verify the performance of S-ESN, three virtual time series Sin time series with low frequency, Sin time series with high frequency, Mackey-Glass time series (MG) and one practical Sunspot are selected as experimental data. The experimental results show that the S-ESN model has better prediction accuracy.

Optimal self-tuning fractional order fuzzy load frequency control considering sustainable energy sources and electric vehicle

Considering uncertainties associated with sustainable energy sources in modern power systems, one of the main obstacles for load frequency control (LFC) is that various publications regard the parameters of FOPID controllers as fixed after optimisation. To tackle this challenge, an adaptive control structure may be a viable option for LFC. In this paper, in a two-zone hybrid power system that includes a thermal power plant, green energy sources, fuel cells, and plug-in electric vehicles, an optimal self-tuning fractional order fuzzy (OSTFOF) controller for LFC is proposed. Pathfinder algorithm (PFA) has been utilised for optimal tuning of parameters of the OSTFOF controller, including the upper bands of output membership functions, the input scaling factors, and the order of integral and derivative operators. In the proposed controller, the values of proportional, integral, and derivative gains are adaptively obtained. In the thermal power plant, non-linear factors, such as governor dead band (GDB) and generation rate constraint (GRC), are considered. Four different scenarios have been considered to evaluate the performance of the proposed OSTFOF controller, and results have been compared with PI, PID, and FOPID controllers. The simulations’ results indicate the excellent performance indices of the suggested OSTFOF controller compared to other controllers.

A Novel Ultra Local Based-Fuzzy PIDF Controller for Frequency Regulation of a Hybrid Microgrid System with High Renewable Energy Penetration and Storage Devices

A new ultra-local control (ULC) model and two marine predator algorithm (MPA)-based controllers; MPA-based proportional-integral-derivative with filter (PIDF) and MPA-based Fuzzy PIDF (FPIDF) controllers; are combined to enhance the frequency response of a hybrid microgrid system. The input scaling factors, boundaries of membership functions, and gains of the FPIDF con-troller are all optimized using the MPA. In order to further enhance the frequency response, the alpha parameter of the proposed ULC model is optimized using MPA. The performance of the pro-posed controller is evaluated in the microgrid system with different renewable energy sources and energy storage devices. Furthermore, a comparison of the proposed MPA-based ULC-PIDF and ULC-FPIDF controllers against the previously designed controllers is presented. Moreover, a vari-ety of scenarios are studied to determine the proposed controller’s sensitivity and robustness to changes in wind speed, step loads, solar irradiance, and system parameter changes. The results of time-domain simulations performed in MATLAB/SIMULINK are shown. Finally, the results demonstrate that under all examined conditions, the new ULC-based controllers tend to further enhance the hybrid microgrid system’s frequency time response.

Parameter Optimization in a Leaky Integrator Echo State Network with an Improved Gravitational Search Algorithm

In the prediction of a nonlinear time series based on a leaky integrator echo state network (leaky-ESN), building a reservoir related to the specific problem is a key step. For problems such as poor performance of randomly generated reservoirs, it is tough to determine the parameter values of the reservoirs. The work in this paper uses the gravitational search algorithm (GSA) to optimize the global parameters of a leaky-ESN, such as the leaking rate, the spectral radius, and the input scaling factor. The basic GSA has some problems, such as slow convergence and poor balance between exploration and exploitation, and it cannot solve some complex optimization problems well. To solve these problems, an improved gravitational search algorithm (IGSA) is proposed in this paper. First, the best agent and elite agents were archived and utilized to accelerate the exploration phase and improve the convergence rate in the exploitation phase. Second, to improve the effect of the poor fitness agents on the optimization result, a differential mutation strategy was proposed, which generated new individuals to replace original agents with worse fitness, increasing the diversity of the population and improving the global optimization ability of the algorithm. Finally, two simulation experiments showed that the leaky-ESN optimized by the IGSA had better prediction accuracy.

Particle Swarm Optimization Algorithm-Tuned Fuzzy Cascade Fractional Order PI-Fractional Order PD for Frequency Regulation of Dual-Area Power System

This study proposes a virgin structure of Fuzzy Logic Control (FLC) for Load Frequency Control (LFC) in a dual-area interconnected electrical power system. This configuration benefits from the advantages of fuzzy control and the merits of Fractional Order theory in traditional PID control. The proposed design is based on Fuzzy Cascade Fractional Order Proportional-Integral and Fractional Order Proportional-Derivative (FC FOPI-FOPD). It includes two controllers, namely FOPI and FOPD connected in cascade in addition to the fuzzy controller and its input scaling factor gains. To boost the performance of this controller, a simple and powerful optimization method called the Particle Swarm Optimization (PSO) algorithm is employed to attain the best possible values of the suggested controller’s parameters. This task is accomplished by reducing the Integral Time Absolute Error (ITAE) of the deviation in frequency and tie line power. Furthermore, to authenticate the excellence of the proposed FC FOPI-FOPD, a comparative study is carried out based on the obtained results and those from previously published works based on classical PID tuned by the Losi Map-Based Chaotic Optimization Algorithm (LCOA), Fuzzy PID Optimized by Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy PID with a filtered derivative mode tuned by PSO, which is employed in the same interconnected power system. The robustness of the suggested fuzzy structure is investigated against the parametric uncertainties of the testbed system. The simulation results revealed that the proposed FC FOPI-FOPD is robust, and it outperformed the other investigated controllers. For example, the drops in the frequency in area one and area two were improved by 89.785% and 97.590%, respectively, based on employing the proposed fuzzy configuration compared with the results obtained from the traditional PID.

Stable optimal self-tuning interval type-2 fuzzy controller for servo position control system

An optimal self-tuning interval type-2 fuzzy controller for servo position control systems is reported here. To achieve precise positioning of the actuator, input scaling factors of an interval type-2 fuzzy proportional-integral controller are updated online depending on the latest operating conditions in terms of closed loop tracking error and change of error. To ensure desired performance, input scaling factors are obtained by adaptive cuckoo search-based optimisation algorithm. Efficacy of the proposed scheme is substantiated through performance comparison with recently reported peak observer based and online self-tuning based interval type-2 fuzzy PID, interval type-2 fuzzy PI, and also type-1 fuzzy PI controllers through simulation study along with real-time validation on a DC servo position control system. Lyapunov function-based stability analysis for the proposed controller is also provided.

Fuzzy Logic-Based Self-Tuning PID Controllers Using Parameters Adaptive Method for Stabilization of a Two-Axis Seeker Gimbal

In guided missiles, the major function of a seeker is to track a target continuously. In target tracking process most seekers employ a gimbal system, usually a two-axis, on which a tracking device and a rate gyroscope are mounted. For such guided missiles, the stabilization of gimbal is essential for maintaining the tracking device directing towards the target. Various control methods have been developed for the gimbal stabilization to make the gimbal to respond faster for a rate command input so that the gimbal follows the rate input in the presence of disturbances. Here, a fuzzy self-tuning proportional, integral, derivative (PID) type controller based on parameters adaptive method is proposed for two-axis gimbal stabilization which can be applicable for most guided missiles. The controller gains in the proposed control technique are made of input and output scaling factors used in fuzzification and defuzzification processes. The scaling factors are tuned online based on the missile rotation rate and the gimbal rate response. The response of the gimbal in the stabilization loop with the proposed controller has been analyzed in MATLAB for various missile rotation rates and the simulation results are compared with a traditional PID controller. The results prove the efficiency of the proposed control method which gives a better and faster response with zero steady-state error than the traditional control method.

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK.

Unrestricted geometric distance functions and the Geometric Young productivity index: an analysis of Indian manufacturing

In this paper, we employ the less frequently used Geometric Young index to measure productivity change. We reformulate the Geometric Distance Function of Portela and Thanassoulis (J Product Anal 25:25–41, 2006) by removing the restrictions on the individual input scaling factors and the output scaling factors to obtain the benchmark input–output bundle on the frontier. In this reformulated model, the Geometric Young index is exhaustively decomposed into the technical efficiency change and technical change factors even under variable returns to scale. Further, between any two periods, the measured rate of technical change is identical across all units. In our empirical analysis, we use state-level data from the Annual Survey of Industries for the period 2010–2011 through 2016–2017 to measure total factor productivity growth in Indian manufacturing and provide its decomposition for the major manufacturing states in India. At the All-India level, the annual average Geometric Young productivity index shows a modest growth over the sample period. This resulted from an improvement in technical efficiency reinforced by a very modest increase in the Technical Change Index.

Nature-inspired and hybrid optimization algorithms on interval Type-2 fuzzy controller for servo processes: a comparative performance study

In this paper, performance evaluations of six well-known nature-inspired algorithms have been reported containing genetic algorithm, cuckoo search, particle swarm optimization, differential evolution, bee colony, and combined particle swarm optimization and differential evolution (CPSODE) algorithms. Based on these optimization algorithms, input and output scaling factors of an interval Type-2 fuzzy PID controller (IT2-FLC) are chosen for closed-loop servo tracking. Optimal values of the scaling factors are chosen by minimization of the objective function which is defined based on the closed-loop controller performance criteria. A detailed comparative analysis is reported based on the simulation and experimental results. Performance analysis reveals that improved performance, reliability, robustness, and lesser noise sensitivity are reported by IT2-FLC with the optimal values obtained by the hybrid algorithm CPSODE.

Application of Fuzzy-PIDF Controller for AGC of A Two-Area Interconnected Thermal Power System under Deregulated Environment

This paper presents the application of fuzzy PID controller and fuzzy PID controller aided with derivative filter (fuzzy PIDF) to analyse the automatic generation control (AGC) issue of a two-area interconnected multi-unit thermal power system having reheat type turbine under deregulated market scenario. This paper, demonstrates the traditional AGC of two-area power system modified under different transactions like as POOLCO based transaction, transaction under bilateral agreement and contract violation transaction to study the system dynamics. Hybrid LUS (Local Unimodal Sampling) and TLBO (Teaching Learning Based Optimization) (LUS-TLBO) technique is proposed to optimize the input and output scaling factors i.e. gains of fuzzy PID controller and gains & filter co-efficient of the derivative filter of the proposed fuzzy PIDF controller under different transactions in the competing market. Comparative performance analysis is carried out to show the supremacy of the proposed fuzzy PIDF controller against proposed fuzzy PID controller and a recently published work on integral controller with Interline Power Flow Controller (IPFC) and Redox Flow batteries (RFB) units.

Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Air–fuel ratio is a key factor for the minimization of the harmful pollutant emissions and maximization of fuel economy. However, a big challenge for air–fuel ratio control is a large time-varying delay existing in spark ignition engines. In this article, a digital fuzzy sliding-mode controller is proposed to control a linear parameter-varying sampled-data air–fuel ratio system. First, the Pade first-order technique is utilized to approximate the time-varying delay. The resultant system—a linear parameter-varying continuous-time air–fuel ratio system with unstable internal dynamics—is then discretized to a linear parameter-varying sampled-data air–fuel ratio system appropriate for a discrete-time control approach. Based on the linear parameter-varying sampled-data air–fuel ratio system, a stable sliding surface with a desired tracking error dynamics is presented. Two input scaling factors and one output scaling factor are determined for the proposed digital fuzzy sliding-mode controller. Then, the fuzzy inference is executed through a look-up table to stabilize the sliding surface into a convex set, and then make the tracking error possess uniformly ultimately bounded performance. The overall system stability is verified by Lyapunov’s stability criteria. Finally, the simulation results demonstrate the feasibility, effectiveness, and robustness of the proposed control scheme under different operating conditions and show the superiority of the proposed approach performance compared to the baseline controller.

A New Robust Fuzzy-PID Controller Design Using Gravitational Search Algorithm

This paper proposes the design of a novel robust load frequency control (LFC) strategy-based optimised fuzzy-PID controller employing gravitational search algorithm (GSA). The suggested GSA algorithm was applied to optimise the input scaling factors of the fuzzy logic and the PID controller gains. To show the potential of the proposed control methodology, a multi-sources two-area interconnected power system was investigated for the simulation. The considered test system comprises various power generating units from hydro, thermal and nuclear sources in area-1, and power generation from hydro, nuclear and diesel sources in area-2. Initially, the simulation was carried considering a centralised controller for both areas to cope with load changes, and then was extended with decentralised controller. Further, sensitivity analysis was performed to demonstrate the ability of the proposed approach in face of wide changes in system parameters and position of load changes. The frequency deviations and the tie-line power flow change were presented, and the superiority of the proposed control strategy was demonstrated by comparing the results with individual gravitational search algorithm (GSA), fuzzy logic controller (FLC) and with some reported techniques in the literature such as Ziegler-Nichols, genetics algorithm (GA), bacterial foraging optimisation algorithm (BFOA) and particle swarm optimisation (PSO).

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>

Experimental Analysis of Simplified Rules Fuzzy Logic Speed Controller for Wide Speed Range Operations

<p>This paper presents the experimental analysis of simplified rules Fuzzy Logic Speed Controller (FLSC) of Induction Motor drive. The maximum gain of input scaling factor, FLSC is generally limited by the coverage of universe of discoursed (UoD). Thus, to further increase the input gain scaling factor, the outer membership function need to be increased. This analysis covers various values in the range of UoD values from [-1,1] to [-5,5] for the wide speed range operations from low to rated speed ranges. The FLSC is employed to the indirect Field Oriented Control method fed by a voltage source inverter. Simulation and experimental verification is done by using Matlab/Simulink and dSPACE 1103 controller experimental rigs respectively. Based on the results, speed performance behaviours are improved over the wide speed range operations in term of rise time and setting time. The tuning approached is simple without additional algorithm for faster and more accurate response.</p>

Operational modal analysis of reinforced concrete bridges using autoregressive model

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

Heading Control for a Robotic Dolphin Based on a Self-Tuning Fuzzy Strategy

In this paper, a heading controller based on a self-tuning fuzzy strategy for a robotic dolphin is proposed to improve control accuracy and stability. The structure of the robotic dolphin is introduced and the turning motion is analysed. The analytic model indicates that the turning joint angle can be employed for the heading control. This non-linear model prevents the successful application of traditional model-based controllers. A fuzzy controller is proposed to realize the heading control in our work. It should be mentioned that the traditional fuzzy controller suffers from a distinguished steady-state error, due to the fact that the heading range is relatively large and the fuzzy controller's universe of discourse is fixed. To resolve this problem, a self-tuning mechanism is employed to adjust the input and output scaling factors according to the active working region in pursuit of favourable performance. Experimental results demonstrate the performance of the proposed controller in terms of steady-state error and robustness to interferences.

Optimal Tuning of Fuzzy Logic Power System Stabilizer Using Harmony Search Algorithm

In this paper, the design of fuzzy logic power system stabilizer (FPSS) is carried out using a harmony search algorithm (HSA) to optimize the input–output scaling factors of the fuzzy logic controller. The optimization problem is considered minimization of integral square error as an objective function with single-machine and multimachine power system. The performance of the HSA optimized FPSS with both systems is compared that of with robust FPSS; HSA tuned input scaling factors of FPSS, and particle swarm optimization tuned input–output FPSS for a wide range of operating conditions. The speed response performance for both systems is compared in terms of performance indices, and superiority of the proposed HSFPSS is validated.

An Auto-Tuning Fuzzy PI Controller Using Subtractive Clustering For Spherical Tank Process – A Real-Time Performance Evaluation

In this paper, a simple and effective method for tuning of fuzzy PI controller based on fuzzy logic with subtractive clustering which minimizes the number of rules is proposed. The tuning of input scaling factors is done by gain updating factors determined by rule base which is designed based on the performance measures such as peak overshoot, rise time and amplitude. The subtractive clustering method is used to reduce the fuzzy inference rules of the three fuzzy reasoning blocks, namely rule base for α, rule base for β, and control rule base. The real-time performance comparison of auto tuned fuzzy PI with and without subtractive clustering is done in terms of several performance measures such as settling time, rise time and integral square error. To show the effectiveness and robustness of the proposed auto tuning mechanism, the analysis is carried out on a spherical tank process, which operates over a wide range and non-linear in nature. Also, the results show that the computational time and the memory requirements are reduced to a great extent for the subtractive clustering approach.