Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness

Universe-inspired algorithms for control engineering: A review

Control algorithms have been proposed based on knowledge related to nature-inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potential Fields was identified, as well as four optimization metaheuristics: Gravitational Search Algorithm, Black-Hole algorithm, Multi-Verse Optimizer, and Galactic Swarm Optimization. A thorough analysis of ninety-one relevant papers was carried out to highlight their performance and to identify the gravitational and attraction foundations, as well as the universe laws supporting them. Included are their standard formulations, as well as their improved, modified, hybrid, cascade, fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves into the impact of universe-inspired algorithms on control problems of dynamic systems, providing an extensive list of control-related applications, and their inherent advantages and limitations. Strong evidence suggests that gravitation-inspired and black-hole dynamic-driven algorithms can outperform other well-known algorithms in control engineering, even though they have not been designed according to realistic astrophysical phenomena and formulated according to astrophysics laws. Even so, they support future research directions towards the development of high-sophisticated control laws inspired by Newtonian/Einsteinian physics, such that effective control-astrophysics bridges can be established and applied in a wide range of applications.

A sensorless anti-windup speed control approach to axial gap bearingless motors with nonlinear lumped mismatched disturbance observers

In this paper, sensorless robust speed control with nonlinear lumped mismatched disturbance observers for a permanent magnet type axial gap bearingless motor (AGBM) is designed. Multistage anti-windup-based dynamic surface control combined with integral backstepping control is proposed to control the motor's axial displacement and rotor speed. The approach is against parameter uncertainties and external disturbances, improving steady-state accuracy, eliminating the derivative explosion phenomenon, no chattering problem, and reducing the magnitude of the control system when current saturation occurs. In addition, a novel nonlinear lumped mismatched disturbance observer is proposed to improve the approach under unmodeled dynamics and external disturbances. To obtain high-accuracy tracking control, the control system includes the robust controller combined with the disturbance observers and anticipatory activation of anti-windup (AW) compensation, which means the controller is more complex. Then, to design a sensorless robust speed control for the motor, the rotor position and speed observer require higher accuracy. High-gain back-EMF observer combined with an improved phase-locked loop is proposed to estimate rotor angular position and speed even when the motor speed is reversed. Overall stability of closed-loop system control, including a sensorless speed control approach for motors using back-EMF estimation combined with saturation of the currents and lumped disturbance observers, is mathematically proven. Finally, the simulation results under measurement noise show that the proposed control system are obtained the effectiveness, feasibility, and robustness.

A novel PID controller for pressure control of artificial ventilator using optimal rule based fuzzy inference system with RCTO algorithm

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60-80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80-90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

Application of multi-SVM classifier and hybrid GSAPSO algorithm for fault diagnosis of electrical machine drive system

A method, being based on multi-class support vector machine (SVM) classifier and hybrid particle swarm optimization (PSO) and gravity search algorithm (GSA), is presented to diagnose the faults in electrical motor drive system. In this method, the global search ability of PSO and the local search ability of GSA are integrated to combine the advantages of PSO and GSA, and the multi-class SVM classifier is optimized by the hybrid GSAPSO algorithm to improve classification performance. To test the presented method, a series of simulation and experiment are studied. The diagnostic results display that the presented method can gain more precise classification accuracy than multi-class SVM with PSO and multi-class SVM with GSA.

A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.

Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network

BACKGROUND AND OBJECTIVE

In this study, we developed a computer controlled treadmill system using a recurrent fuzzy neural network heart rate controller (RFNNHRC). Treadmill speeds and inclines were controlled by corresponding control servo motors. The RFNNHRC was used to generate the control signals to automatically control treadmill speed and incline to minimize the user heart rate deviations from a preset profile.

METHODS

The RFNNHRC combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties. Treadmill speeds and inclines are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations. The on-line learning algorithm of RFNNHRC was developed and implemented using a dsPIC 30F4011 DSP.

RESULTS

Application of the proposed control scheme to heart rate responses of runners resulted in smaller fluctuations than those produced by using proportional integra control, and treadmill speeds and inclines were smoother. The present experiments demonstrate improved heart rate tracking performance with the proposed control scheme.

CONCLUSIONS

The RFNNHRC scheme with adjustable parameters and an on-line learning algorithm was applied to a computer controlled treadmill system with heart rate control during treadmill exercise. Novel RFNNHRC structure and controller stability analyses were introduced. The RFNNHRC were tuned using a Lyapunov function to ensure system stability. The superior heart rate control with the proposed RFNNHRC scheme was demonstrated with various pre-set heart rates.