Firefly Algorithm (FA) refers to a swarm intelligence-based technique which appears to be one of the most influential optimization algorithms in designing optimized controllers for power converters in recent years. Hence, an optimized PID controller is designed based on this algorithm, and stability analysis and performance enhancement of the closed-loop Single-Ended Primary Inductor Converter (SEPIC) are demonstrated comprehensively. Using the state space average technique, the SEPIC converter is mathematically modeled, and the transfer function for the closed-loop system is obtained. The performance parameters noted in the analysis of the system's stability are the percentage of overshoot (%OS), rise time (Tr), settling time (Ts), and peak amplitude (PA). However, fitness functions like integral absolute error (IAE), integral squared error (ISE), integral time squared error (ITSE), integral time absolute error (ITAE) are considered in this optimization process. Step responses based on the gain parameters are attained, and a comparative study with performance evaluation is shown between the optimized and conventional PID controller. MATLAB and Simulink are utilized for simulation.

This article examines the performance of a single-phase PWM inverter equipped with a PI and PID controller. The research aims to decrease Total Harmonic Distortion (THD) in case of fixed and variable output voltages. To reduce THD, PI and PID controllers are used, which are tuned using PID tuner and Trial and Error methods, and the THD is measured using the Simulink powergui’s FFT analysis tool. MATLAB Simulink is used for inverter modelling and simulation. For PI controller-based Inverter, the THD is 0.45% for fixed 400V output voltage and it is increased to 50.85% for rapidly changed output voltage. Furthermore, for PID controller-based Inverter, the THD is 0.01% for fixed 400V output voltage and it is increased to 52.41% for rapidly changed output voltage. So, PI controller-based inverter works better for rapidly changing output voltage, but the PID controller-based inverter performs better for fixed output voltage, according to the comparison analysis.

This paper represents an inspective work on the closed-loop stability of the Bidirectional SEPIC converter by implementing the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) for scheming an optimized PID controller to improve the Percentage of Overshoot (%OS), Rise Time (Tr), Settling Time (Ts), and Peak Amplitude (PA). At first, the transfer function of the converter’s open-loop system was determined after which the converter’s closed-loop arrangement was introduced with the controller’s help. The algorithms were evaluated in the system through different fitness functions: IAE, ITAE, ISE, and ITSE. MATLAB is used to carry out all the simulations. After analyzing the performances for the case of the Bidirectional SEPIC converter, PSO-PID (ITSE) could be chosen as a more optimized controller than FA-based PID controllers as to performance parameters. In this case, values of overshoot (0.2674%) and PA (1.0027) were lower than FA-PID for error function. For rise time, the value of FA-PID (ITAE) was better (0.0670 sec), and for settling time, the value of FA-PID (ITSE) was better (0.1217 sec). Hence, Swarm Intelligence Algorithms-based optimized PID controller provides more optimized results and performs far better than conventional PID controller for Bidirectional SEPIC converter.

This paper presents the development of a novel control algorithm designed for tasks involving human-robot collaboration. By using an 8-DOF robotic arm, our approach aims to counteract human-induced uncertainties added to the robot’s nominal trajectory. To address this challenge, we incorporate a variable within the regular Model Predictive Control (MPC) framework to account for human uncertainties, which are modeled as following a normal distribution with a nonzero mean and variance. Our solution involves formulating and solving an uncertainty-aware Discrete Algebraic Ricatti Equation (ua-DARE), which yields the optimal control law for all joints to mitigate the impact of these uncertainties. We validate our methodology through theoretical analysis, demonstrating the effectiveness of the ua-DARE in providing an optimal control strategy. Our approach is further validated through simulation experiments using a Fetch robot model, where the results highlight a significant improvement in performance over a baseline algorithm that does not consider human uncertainty while solving for optimal control law.

This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and a manipulator arm, compensates for uncertain human behaviors and internal actuation noise through a two-step iterative process. At each planning horizon, a candidate set of feasible joint configurations is generated using a Conditional Variational Autoencoder (CVAE). From this set, one configuration is selected by minimizing an estimated control cost computed via a disturbance-aware Discrete Algebraic Riccati Equation (DARE), which also provides the optimal control inputs for both the mobile base and the manipulator arm. We derive the disturbance-aware DARE and validate DARC with simulated experiments with a Fetch robot. Evaluations across various trajectories and disturbance levels demonstrate that our proposed DARC framework outperforms baseline algorithms that lack disturbance modeling, pose optimization, or both.

Mutual adaptation can significantly enhance overall task performance in human-robot co-transportation by integrating both the robot's and human's understanding of the environment. While human modeling helps capture humans' subjective preferences, two challenges persist: (i) the uncertainty of human preference parameters and (ii) the need to balance adaptation strategies that benefit both humans and robots. In this paper, we propose a unified framework to address these challenges and improve task performance through mutual adaptation. First, instead of relying on fixed parameters, we model a probability distribution of human choices by incorporating a range of uncertain human parameters. Next, we introduce a time-varying stubbornness measure and a coordination mode transition model, which allows either the robot to lead the team's trajectory or, if a human's preferred path conflicts with the robot's plan and their stubbornness exceeds a threshold, the robot to transition to following the human. Finally, we introduce a pose optimization strategy to mitigate the uncertain human behaviors when they are leading. To validate the framework, we design and perform experiments with real human feedback. We then demonstrate, through simulations, the effectiveness of our models in enhancing task performance with mutual adaptation and pose optimization.