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In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral–proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation. [ABSTRACT FROM AUTHOR]

The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator ( PIDD 2 -PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm (GWO), Cuckoo Search algorithm (CSA), Gradient Base Optimization (GBO), and Whale Optimization Algorithm (WOA). The effectiveness of proposed controllers PIDD 2 -PI and TID-F is investigated to be better in terms of transient time response than proportional integral derivative (PID), proportional integral-derivative (PI-D), proportional integral-proportional derivative (PI-PD) and proportional integral derivative-second derivative-proportional derivative ( PIDD 2 -PD). Moreover, these two proposed controllers have also been compared with recently published work. During the analysis, it is shown that the proposed control strategies exhibit significantly greater robustness and dynamic responsiveness compared to other structural controllers. The proposed controller WOA- PIDD 2 -PI reduced the 73.38% settling time and 88.16% rise time compared to classical PID. The other proposed controller GWO-TID-F reduced 58.06% the settling time and 26.96% rise time compared to classical PID. These results show that proposed controllers are particularly distinguished in terms of rise time, settling time, maximum overshoot, and set-point tracking. [ABSTRACT FROM AUTHOR]

This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller's steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure. [ABSTRACT FROM AUTHOR]

Existing interconnected power systems (IPSs) are being overloaded by the expansion of the industrial and residential sectors together with the incorporation of renewable energy sources, which cause serious fluctuations in frequency, voltage, and tie-line power. The automatic voltage regulation (AVR) and load frequency control (LFC) loops provide high quality power to all consumers with nominal frequency, voltage, and tie-line power deviation, ensuring the stability and security of IPS in these conditions. In this paper, a proportional integral derivative (PID) controller is investigated for the effective control of a four-area IPS. Each IPS area has five generating units including gas, thermal reheat, hydro, and two renewable energy sources, namely wind and solar photovoltaic plants. The PID controller was tuned by a meta-heuristic optimization algorithm known as a gradient-based optimizer (GBO). The integral of time multiplied by squared value of error (ITSE) was utilized as an error criterion for the evaluation of the fitness function. The voltage, frequency, and tie-line power responses of GBO-PID were evaluated and compared with integral–proportional derivative (GBO-I-PD), tilt integral derivative (GBO-TID), and integral–proportional (GBO-I-P) controllers with 5% step load perturbation (SLP) provided in each of the four areas. Comprehensive comparisons between GBO-PID and other control methodologies revealed that the proposed GBO-PID controller provides superior voltage, frequency, and tie-line power responses in each area. The reliability and efficacy of GBO-PID methodology were further validated with variations in the turbine time constant and speed regulation over a range of  ± 25%. It is evident from the outcomes of the sensitivity analysis that the proposed GBO-PID control methodology is very reliable and can successfully stabilize the deviations in terminal voltage, load frequency, and tie-line power with a shorter settling time in a four-area IPS. [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]