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This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid.

To evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out using MATLAB/SIMULINK package.

Simulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions.

In this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.

Considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.

The authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.

This paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.

An augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

To show the successful use of self‐organising fuzzy control in enhancing dynamic optimisation, a controller is used to direct the type of optimisation appropriate in each new dynamic problem. The system uses its experiences to determine which approach is most suitable under varying circumstances.

A knowledge extraction tool is used to gain basic information about the solution space with a simple computation. This information is compared with the fuzzy rules stored in the system. These rules hold a collection of facts on previous successes and failures, which were acquired through the performance monitor. Using this system the controller directs the algorithms, deciphering the most appropriate strategy for the current problem.

This procedure is designed for large scale dynamic optimisation problems, where a portion of the computational time is sacrificed to allow the controller to direct the best possible solution strategy. The results here are based on smaller scale systems, which illustrate the benefits of the technique.

The results highlight two significant aspects. From the comparison of the three algorithms without the use of the controller, a pattern can be seen in how the algorithms perform on different types of problems. Results show an improvement in the overall quality when the controller is employed.

This paper introduces a novel approach to the problem dynamic optimisation. It combines the control ability of self‐organising fuzzy logic with a range of optimisation techniques to obtain the best possible approach in any one situation.

This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

This paper aims to improve the adaptability and control performance of the controller, a proposed seeker optimization algorithm (SOA) is introduced to optimize the controller parameters of a robot manipulator.

In this paper, a traditional proportional integral derivative (PID) controller and a fuzzy logic controller are integrated to form a fuzzy PID (FPID) controller. The SOA, as a novel algorithm, is used for optimizing the controller parameters offline. There is a performance comparison in terms of FPID optimization about the SOA, the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The DC motor model and the experimental platform are used to test the performance of the optimized controller.

Compared with GA, PSO and ACO, this novel optimization algorithm can enhance the control accuracy of the system. The optimized parameters ensure a system with faster response speed and better robustness.

A simplified FPID controller structure is constructed and a novel SOA method for FPID controller is presented. In this paper, the SOA is applied on the controller of 5-DOF manipulator, and the validation of controllers is tested by experiments.

The purpose of this paper is to study the application of entropy based optimized fuzzy logic control for a real-time non-linear system. Optimization of the fuzzy membership function (MF) is one of the most explored areas for performance improvement of the fuzzy logic controllers (FLC). Conversely, majority of previous works are motivated on choosing an optimized shape for the MF, while on the other hand the support of fuzzy set is not accounted.

The proposed investigation provides the optimal support for predefined MFs by using genetic algorithms-based optimization of fuzzy entropy-based objective function.

The experimental results obtained indicate an improvement in the performance of the controller which includes improvement in error indices, transient and steady-state parameters. The applicability of proposed algorithm has been verified through real-time control of the twin rotor multiple-input, multiple-output system (TRMS).

The proposed algorithm has been used for the optimization of triangular sets, and can also be used for the optimization of other fussy sets, such as Gaussian, s-function, etc.

The proposed optimization can be combined with other algorithms which optimize the mathematical function (shape), and a potent optimization tool for designing of the FLC can be formulated.

This paper presents the application of a new optimized FLC which is tested for control of pitch and yaw angles in a TRMS. The performance of the proposed optimized FLC shows significant improvement when compared with standard references.

This paper aims to implement a maiden methodology for load frequency control of an AC multi micro-grid (MG) by using hybrid fractional order fuzzy PID (FOFPID) controller and linear quadratic Gaussian (LQG).

The multi MG system considered is consisting of photovoltaic, wind turbine and a synchronous generator. Different energy storage devices i.e. battery energy storage system and flywheel energy storage system are also integrated to the system. The renewable energy sources suffer from uncertainty and fluctuation from their nominal values, which results in fluctuation of system frequency. Inspired by this difficulty in MG control, this research paper proposes a hybridized FOFPID and LQG controller under random and stochastic environments. Again to confer viability of proposed controller its performances are compared with PID, fuzzy PID and fuzzy PID-LQG controllers. A comparative study among all implemented techniques i.e. proposed multi-verse optimization (MVO) algorithm, particle swarm optimization and genetic algorithm has been done to justify the supremacy of MVO algorithm. To check the robustness of the controller sensitivity analysis is done.

The merged concept of fractional calculus and state feedback theory is found to be efficient. The designed controller is found to be capable of rejecting the effect of disturbances present in the system.

From the study, the authors observed that the proposed hybrid FOPID and LQG controller is robust hence, there is no need to reset the controller parameters with a large change in network parameters.

A fuzzy controller is considered which attempts to control a process which has multiple outputs and inputs, towards some set points. The fuzzification/ defuzzification procedures, the fuzzy logic and the fuzzy control rules are considered and are chosen so that the defuzzified output is always a linear function of the inputs to the fuzzy controller.

The purpose of this paper is to control a landing gear system with an oleo-pneumatic shock absorber with the fuzzy controller.

The landing gear system with an oleo-pneumatic shock absorber is modeled mathematically. A fuzzy controller is designed for reducing aircraft vibrations. Stroke velocity and main mass velocity parameters were used to decide variable gas pressure with the fuzzy controller.

The fuzzy controller, designed according to stroke velocity and main mass velocity, reduces aircraft vibrations by the landing impacts. The controller can provide strong robustness because it shows similar good performance for different descent speeds.

This study was carried out through simulations in a computer environment and has not been experimentally tested in a real environment. In addition, signal and measurement delays are not taken into account. In future models, the effects of these signal delays can be added, and the controller can be tested on a real model.

In this study, to the best of the authors’ knowledge, for the first time, the gas pressure for the landing gear system using an oleo-pneumatic shock absorber was controlled by a fuzzy controller that adjusts the stroke velocity and the main mass velocity. Although the oleo-pneumatic shock absorber model contains high nonlinearities, the designed fuzzy controller gave successful results as robust.

In recent years it has been illustrated that the Unified Power Flow Controller (UPFC) installation location plays an important role in effecting nonlinearly its steady state performance. A Pulse Width Modulation (PWM) based UPFC used as a voltage regulator is modeled and analyzed to investigate its optimal position in the transmission line. From the simulation results it is demonstration that by varying the modulation index of the device it can control the distribution of the active and reactive power flows. In addition, this paper deals with the definition and simulation of the control strategy of the closed‐loop UPFC with a series compensation block when it operates as a terminal voltage regulator using Electromagnetic Transients Program (EMTP). The design and simulation of two types of digital controller strategies for the study system in this paper have been carried out. The dynamic performance in terms of speed stability, accuracy, robustness and simplicity of a PI controller with gain scheduling and a fuzzy logic controller have been tested and compared.