Safe Experimentation Dynamics Research Articles

Identification of continuous-time Hammerstein model using improved Archimedes optimization algorithm

Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. The proposed algorithm employed two principal modifications to mitigate these issues and enhance identification accuracy: (i) exploration and exploitation phase recalibrations using a revised density decreasing factor and (ii) local optima entrapment alleviation utilizing safe experimentation dynamics. Various advantages were observed with this proposed algorithm, including a lower number of coefficient criteria, improved accuracy in Hammerstein model identification, and diminished processing demands by reducing gain redundancy between nonlinear and linear subsystems. This proposed algorithm also discerned linear and nonlinear subsystem variables within a continuous-time Hammerstein model utilizing input and output data. The process was evaluated using a numerical example and two practical experiments [twin-rotor system (TRS) and electro-mechanical positioning system (EMPS)]. Several parameters were then analyzed, such as the convergence curve of the fitness function, frequency and time domain-related responses, variable deviation index, and Wilcoxon's rank-sum test. Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74 % mean fitness function and 75.34 % variable deviation indices enchantments compared to the traditional AOA. Improved mean fitness function values were also revealed in the TRS (11.63 %) and EMPS (69.63 %) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm).

Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model

The use of cupping therapy for various health benefits has increased in popularity recently. Potential advantages of cupping therapy include pain reduction, increased circulation, relaxation, and skin health. The increased blood flow makes it easier to supply nutrients and oxygen to the tissues, promoting healing. Nevertheless, the effectiveness of this technique greatly depends on the negative pressure's ability to create the desired suction effect on the skin. This research paper suggests a method to detect the cupping suction model by employing the Hammerstein model and utilizing the Safe Experimentation Dynamics (SED) algorithm. The problem is that the cupping suction system experiences pressure leaks and is difficult to control. Although, stabilizing the suction pressure and developing an effective controller requires an accurate model. The research contribution lies in utilizing the SED algorithm to tune the parameters of the Hammerstein model specifically for the cupping suction system and figure out the real system with a continuous-time transfer function. The experimental data collected for cupping therapy exhibited nonlinearity attributed to the complex dynamics of the system, presenting challenges in developing a Hammerstein model. This work used a nonlinear model to study the cupping suction system. Input and output data were collected from the differential pressure sensor for 20 minutes, sampling every 0.1 seconds. The single-agent method SED has limited exploration capabilities for finding optimum value but excels in exploitation. To address this limitation, incorporating initial values leads to improved performance and a better match with the real experimental observations. Experimentation was conducted to find the best model parameters for the desired suction pressure. The therapy can be administered with greater precision and efficacy by accurately identifying the suction pressure. Overall, this research represents a promising development in cupping therapy. In particular, it has been demonstrated that the proposed nonlinear Hammerstein models improve accuracy by 84.34% through the tuning SED algorithm.

An Improved Marine Predators Algorithm-Tuned Fractional-Order PID Controller for Automatic Voltage Regulator System

One of the most popular controllers for the automatic voltage regulator (AVR) in maintaining the voltage level of a synchronous generator is the fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning the FOPID controller is challenging since there are five gains compared to the three gains of a conventional proportional–integral–derivative (PID) controller. Therefore, this research work presents a variant of the marine predators algorithm (MPA) for tuning the FOPID controller of the AVR system. Here, two modifications are applied to the existing MPA: the hybridization between MPA and the safe experimentation dynamics algorithm (SEDA) in the updating mechanism to solve the local optima issue, and the introduction of a tunable step size adaptive coefficient (CF) to improve the searching capability. The effectiveness of the proposed method in tuning the FOPID controller of the AVR system was assessed in terms of the convergence curve of the objective function, the statistical analysis of the objective function, Wilcoxon’s rank test, the step response analysis, stability analyses, and robustness analyses where the AVR system was subjected to noise, disturbance, and parameter uncertainties. We have shown that our proposed controller has improved the AVR system’s transient response and also produced about two times better results for objective function compared with other recent metaheuristic optimization-tuned FOPID controllers.

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size ( CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

ABSTRACT The current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. Reciprocated synergism between both meta-heuristic algorithms is achieved by appropriating the nonlinear position-updated mechanism of NSCA for enhanced exploration/exploitation competencies and proficiency of SED in maximizing stagnation avoidance within the local optima. Named the NSCA-SED algorithm, the applicability of the proposed method is assessed by scholastic adoption of a sixth-order numerical transfer function towards two independent high-order systems enclosing Double-Pendulum Overhead Crane and Flexible Manipulator. Experimentation results further suggested NSCA-SED as the superior alternative in terms of execution robustness and consistency excellence against other available optimization-based methods for tackling model order reduction. Exemplified simulations sequentially demonstrated considerable improvements by the employment of NSCA-SED over conventional SCA following respective enhanced proportions of 97.17%, 13.17% and 29.03% for Example 1, Example 2 and Example 3.

PID Tuning Method Using Chaotic Safe Experimentation Dynamics Algorithm for Elastic Joint Manipulator

This paper proposed the chaotic safe experimentation dynamics algorithm (CSEDA) to regulate angular tracking and vibration of the self-tuning PID controller for elastic joint manipulators. CSEDA was a modified version of the safe experimentation dynamics algorithm (SEDA) that used a chaos function in the updated equation. The chaos function increased the exploration capability, thus improving the convergence accuracy. In this study, two self-tuning PID controllers were used to regulate the rotating angle tracking and vibration for elastic joint manipulators in this control challenge. The suggested self-tuning PID controller's performance was evaluated in angular motion trajectory tracking, vibration suppression, and the pre-determined control fitness function. A self-tuned PID controller based on CSEDA could achieve superior control accuracy than a traditional SEDA and its variants.

Sigmoid PID based adaptive safe experimentation dynamics algorithm of portable duodopa pump for Parkinson’s disease patients

This paper emphasizes on the development of an appropriate closed-loop control strategy for traditional portable duodopa pump (PDP); thereby ensuring an automated drug infusion without wearing off. In particular, a sigmoid proportional integral derivative (SPID) controller is adopted to control the blood plasma level of dopamine. The parameters of SPID controller are tuned using the adaptive safe experimentation dynamics (ASED) algorithm. The efficiency of the suggested SPID-ASED is evaluated by concerning the convergence plot of the cost function, the amount of dopamine in the blood plasma (BP) of the patient, the statistical analysis of cost function, norm of error and norm of input, and time responses specifications. The simulation results show that the proposed SPID-ASED outperforms the standard PID controller in terms of better control accuracy with minimum overshoot and settling time.

Data-based PID control of flexible joint robot using adaptive safe experimentation dynamics algorithm

This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.

A Multiple-node Hormone Regulation of Neuroendocrine-PID (MnHR-NEPID) Control for Nonlinear MIMO Systems

This paper proposed a multiple-node hormone regulation neuroendocrine-proportional-integration- differential (MnHR-NEPID) controller based on adaptive safe experimentation Dynamics (ASED) for nonlinear multi-input-multi-output (MIMO) systems. In the existing neuroendocrine-PID (NEPID) controller of the single-input-single-output (SISO) system, only a single node of hormone regulation is used due to a single control variable. Meanwhile, in the MIMO system, since having many control variables, it is worth introducing an MnHR-NEPID for better controller performance by prioritizing each node's control regulation from their level of error. In particular, instead of considering its own hormone regulation, each node's hormone regulation is also generated based on the change of error from other control variables or nodes if the error of that corresponding control variable exceeds the given error threshold. Here, the relation between hormone regulation and the change of error is adopted based on the normalized Gaussian function. As a result, better prioritize control regulation with heightened control accuracy can be subsequently achieved due to interactions between multiple nodes of hormones available for the nonlinear MIMO system. The performance error and control input for several nonlinear MIMO systems were further tracked to assess the proposed controller's performance. Standard PID, NEPID, and sigmoid-based secretion rate neuroendocrine-PID (SbSR-NEPID) controllers were also compared. Thus, this simulation work has acknowledged higher accuracy within the design of an MnHR-NEPID controller, with comparatively superior objective function and total norm of error resulted in better control performance.

A modified grey wolf optimizer for improving wind plant energy production

<span>The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods.</span>

Data-Driven Neuroendocrine-PID Controller Design for Twin Rotor MIMO System

This paper presents the design of a data-driven neuroendocrine-PID controller based on adaptive safe experimentation dynamics (ASED) method for a twin-rotor MIMO system (TRMS). Neuroendocrine-PID is deemed a compatible controller, often due to its biological-inspired mechanism from a human’s endocrine system that promotes control effectiveness and accuracy. In assessing the robustness of the proposed controller, its parameters were optimized through the ASED method, by tracking both error and input control performances. In particular, the ASED method is a game-theoretic method that randomly perturbs several elements of its controller parameters to search for the optimal controller parameters. Comparison was further made alongside performance of a standard PID controller. Following the simulation conducted, findings with regards to total norm error and total norm input have hereby suggested neuroendocrine-PID as a better controller, following a 13.2% improvement in control accuracy to that of a standard PID controller for TRMS system.

Adaptive Safe Experimentation Dynamics for Data-Driven Neuroendocrine-PID Control of MIMO Systems

A safe experimentation dynamics (SED) is a game theoretic method that randomly perturbs several elements of its design parameter to search for the optimal design parameter. However, the accuracy of the standard SED can be further improved by proposing an adaptive SED method. This paper aims to develop a method based on adaptive safe experimentation dynamics (ASED) where the updated design parameter is modified to adapt to the change of the objective function. The proposed ASED is then used to tune the parameters of the neuroendocrine-PID controller. The neuroendocrine-PID controller, which is based on the secretion rule of hormone regulation in the human body and well known for its high control accuracy, is chosen to improve the conventional PID controller structure for the MIMO systems. Moreover, it is shown that the proposed neuroendocrine-PID based ASED can solve an unstable convergence problem in the existing neuroendocrine-PID based Simultaneous Perturbation Stochastic Approximation (SPSA). The performance of the proposed neuroendocrine-PID based on the ASED method is evaluated by tracking its performances and computational time. Additionally, the performance of the ASED based method is compared to the standard SED and SPSA based methods. The results of the simulation showed that the ASED method could provide stable convergence by minimizing the function of the given objective. The ASED also obtains a value for the objective function and the total norm error for tracking performance accuracy that is lower compared to other methods.

A piecewise affine PI controller for buck converter generated DC motor

<span>This paper presents a new Piecewise Affine Proportional-Integral (PA-PI) controller for angular velocity tracking of a buck converter generated dc motor. A Safe Experimentation Dynamics (SED) algorithm is employed as a data-driven optimization tool to find the optimal PA-PI controller parameters such that the integral square of error and input are reduced. The essential feature of the PA-PI controller is that the parameters of proportional and integral gains are adaptive to the error variations according to the Piecewise Affine (PA) function. Moreover, the proposed PA function is expected to provide better control accuracy than the other existing variable structure PID controller. In order to verify the effectiveness of the PA-PI controller, a widely known buck converter generated dc motor is considered. The performances of the proposed controller are observed in terms of the integral square of error and input, and the responses of the angular velocity and duty ration input. The simulation results verify that the proposed PA-PI controller yields higher control accuracy than the other existing controllers of buck converter generated dc motor.</span>

An improved neuroendocrine–proportional–integral–derivative controller with sigmoid-based secretion rate for nonlinear multi-input–multi-output crane systems

This paper proposes an improved neuroendocrine–proportional–integral–derivative controller for nonlinear multi-input–multi-output crane systems using a sigmoid-based secretion rate of the hormone regulation. The main advantage of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative is that the hormone secretion rate of neuroendocrine–proportional–integral–derivative can be varied according to the change of error. As a result, it can provide high accuracy control performance, especially in nonlinear multi-input–multi-output crane systems. In particular, the hormone secretion rate is designed to adapt with the changes of error using a sigmoid function, thus contributing to enhanced control accuracy. The parameters of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller are tuned using the safe experimentation dynamics algorithm. The performance of the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller-based safe experimentation dynamics algorithm is evaluated by tracking the error and the control input. In addition, the performances of proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers are compared with the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative performance. From the simulation work, it is discovered that the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative design provides better control performances in terms of the objective function, the total norm of error and the total norm of input compared to proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers. In particular, it is shown the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller contributes 5.12% of control accuracy improvement by changing the fixed hormone secretion rate into a variable hormone secretion rate based on the change of error.

A simplify fuzzy logic controller design based safe experimentation dynamics for pantograph-catenary system

Contact force between catenary and pantograph of high speed train is a crucial system to deliver power to the train. The inconsistence force between them can cause the contact wire oscillate a lot and it can damage the mechanical structure of system and produce electric arc that can reduce the performance of system. This project proposes a single-input fuzzy logic controller (SIFLC) to control the contact force between the pantograph-catenary by implement Safe Experimentation Dynamics (SED) method to tune the SIFLC parameters. The essential feature of SIFLC is that it is model-free type controller design with less pre-defined variables as compared to other existing model-based controllers. The performance of the SIFLC is analyzed in terms of input tracking of contact force of pantograph-catenary and time response specifications. A simplified model of three degree of freedom (3-DOF) pantograph-catenary system is considered. In this study, the simulation result shows that the SIFLC successfully track the given contact force with less overshoot with percentage different of peak to peak response from actual force 2% and fast response within 5.27s

A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm

One of the main issues of the wind plant power generation nowadays is that the current stand alone controller of each turbine in the wind plant is not able to cope with chaotic nature of wake aerodynamic effect. Therefore, it is necessary to re-tune the controller of each turbine in the wind plant such that the total power generation is improved. This article presents an investigation of a data driven approach using moth-flame optimization algorithm (MFO) to the problem of improving wind plants power generation. The MFO based technique is applied to search the turbine’s optimum controller such that the aggregation power generation of a wind plant is maximized. The MFO is a population based optimization method that mimics the behavior of moths that navigate on specific angle with respect to the moon location. Here, it is expected that the MFO can solve the control accuracy problem in the existing algorithms for maximizing wind plant. A row of wind turbines plant with wake aerodynamic effect among turbines is adopted to demonstrate the effectiveness of the MFO based technique. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The performance of the proposed MFO algorithm is analyzed in terms of the statistical analysis of the total power generation. Numerical results show that the MFO based approach generates better total wind power generation than spiral dynamic algorithm (SDA) based approach and safe experimentation dynamics (SED) based approach.