Sumeet S. Aphale

Los actuadores neum´aticos blandos presentan fuertes no linealidades que dificultan el control preciso de su movimiento. Entrabajos previos se ha demostrado que la regulaci´on de presi´on mediante modulaci´on de frecuencia de pulso por integral inversa(IIPFM, por sus siglas en ingl´es) mejora significativamente el desempe˜no del lazo interno de control. Este trabajo aborda el controlde flexi´on de un sistema neum´atico blando, centr´andose en el desarrollo del lazo externo de control. Como resultado preliminar, se presenta un algoritmo basado en visi´on artificial que permite la estimaci´on en tiempo real de la curvatura y la posici´on del extremo del actuador, habilitando el cierre del lazo externo en desarrollos futuros.

Pneumatic transmission lines play a critical role in the dynamic performance of soft robotic actuation systems, yet their behaviour is difficult to capture using conventional integer-order (IO) models. In long, slender pipelines, compressibility, viscothermal losses, and wave propagation give rise to distributed damping and non-exponential relaxation dynamics that are not well represented by finite-dimensional models. This paper presents a control-oriented, experimentally validated fractional-order (FO) modelling framework for pneumatic pipeline dynamics under closed-end boundary conditions. Models are calibrated using measured step-response data from a 13.2 m pipeline, with all parameters—including the fractional order—identified through a unified optimisation procedure. In addition to global fitting accuracy, model performance is evaluated using control-relevant metrics, including effective delay, initial slope and early transient behaviour, and early-time error. The results show that FO models provide a more compact and structurally consistent representation of long-memory dynamics while improving the accuracy of control-relevant features compared to their IO counterparts. These findings demonstrate that fractional dynamics offer a physically meaningful and practically useful framework for modelling pneumatic transmission lines, with direct implications for high-performance control design in soft robotic systems.

Solenoid valves are widely used for pressure regulation in soft pneumatic robots, yet their intrinsic electromechanical nonlinearities, including dead-zone, saturation, and pressure-dependent dynamics, pose significant challenges for control design. Conventional pulse-modulated strategies, such as pulse-width modulation (PWM), often aggravate these effects by ignoring the transient behaviour associated with valve switching. This work introduces a spiking-based control strategy inspired by neuromorphic principles and specifically tailored for pneumatic pressure regulation in soft actuators. The proposed method uses a neuron-like modulator in cascade with a conventional controller to effectively shape the valve input, intrinsically compensate dead-zone and saturation, and improve the linearity of the pressure dynamics through parameter tuning. A complete experimental validation is conducted on a soft pneumatic actuator (SPA) driven by a solenoid valve, benchmarking the proposed approach against standard pulse-modulation techniques. The results show substantial improvements in both static and dynamic linearity, reduced nonlinear distortions, and enhanced consistency in closed-loop pressure control across operating points.

This paper presents a capability‐driven sequential optimization framework for the robust design of electrical machines under manufacturing uncertainty. The proposed methodology integrates surrogate modeling, NSGA‐II‐based Pareto front exploration, and Design for Six Sigma (DFSS)‐driven refinement to ensure production‐level reliability. Surrogate models constructed via Response Surface Methodology (RSM) enable efficient trade‐off analysis across key performance metrics, including torque ripple, average torque, efficiency, and power factor. The first optimization stage applies NSGA‐II to identify Pareto‐optimal trade‐offs, while statistical analysis reveals residual variability risks in critical objectives. To address this, the second stage introduces DFSS‐based capability constraints within a sequential refinement process, driving capability indices ( C _{p k} ) above 2.0 and reducing probability of failure (PoF) to below 0.01% across all objectives. Compared to baseline designs, the optimized solutions demonstrate significant reductions in variability: 36% in torque ripple, 40% in power factor, and 27% in torque spread. The proposed framework is validated as an effective and scalable approach for robust optimization of electrical machines subjected to real‐world manufacturing tolerances, enabling production‐ready solutions with high statistical confidence.