arXiv subjects

Mohsen Jalaeian-Farimani

Publications and source records attributed to Mohsen Jalaeian-Farimani.

5 recordsLinked to original sources

arXiv2026

A particle swarm optimization with a reduced-subspace trust-region safeguard for contact-rich controller tuning

Contact-rich robotic insertion is challenging because uncertainty in alignment, friction, and contact conditions can lead to unsuccessful assembly, excessive contact forces, and jamming. Designing a reliable controller therefore requires balancing insertion performance, robustness, and recovery from unexpected contact. In this paper, we propose PSO+TR, a derivative-free optimization (DFO) method that combines the exploratory search of particle swarm optimization (PSO) with a trust-region (TR) procedure for local refinement to improve the search process when progress stagnates. We apply the method to derivative-free policy optimization of an interpretable robotic insertion controller, jointly tuning its motion, feedback, and recovery parameters while preserving its predefined control structure. The proposed approach is evaluated on 93 eligible benchmark problems from the bound-constrained programs (BCP) collection and a synthetic contact-rich planar insertion task under uncertain operating conditions. Three random seeds per benchmark problem yield 279 matched problem--seed instances per method. PSO+TR reaches the prescribed target on 228 of these instances (81.72\%), the highest observed target-attainment rate among the five compared methods, which also include PSO, randomized black-box optimization (VRBBO), matrix adaptation evolution strategy (MAES), and differential evolution (DE). Across eight robotic optimizer seeds, the deployed controller-selection pipeline achieves 97.78\% completion under development conditions and 58.00\% under withheld stress conditions, compared with 91.22\% and 31.12\%, respectively, for the fixed staged controller. The stress violation rate is reduced from 20.69\% to 2.19\%. These findings support combining global and local search for DFO and structured contact-rich controller tuning. The synthetic planar results require validation on physical robots.

math.OC↗

arXiv2026

Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing

We formulate offline tuning of Model Predictive Path Integral (MPPI) controllers as a chance-constrained black-box optimization problem and develop an update-aware framework based on Matrix Adaptation Evolution Strategy (MA-ES). We use statistical information from noisy controller evaluations to determine when the evolutionary update is sufficiently reliable. When the recombination direction is certified, a derivative-free nonmonotone line search (NMLS) is used to refine the candidate solution, and a separate validation stage assesses the final frozen controller. We establish a worst-case arithmetic-complexity bound for both online MPPI control and offline tuning. Numerical experiments on TurtleBot 4, Jackal-like, and Ackermann models show that the proposed NMLS refinement is competitive with standard MA-ES, transfers across the three vehicle models without model-specific retuning, and improves several cost and validation measures. Both MA-ES variants also compare favorably with the tested randomized derivative-free line search (VRBBO) configuration. The results support the practical applicability and transferability of the proposed framework, while not claiming universal superiority or hardware-level safety.

math.OC↗

arXiv2026

ETM-3D: A One-Parameter Multistable Quadratic Flow with an Explicit Absorbing Set and Intermittent Chaos

We study a one-parameter three-dimensional quadratic flow, denoted ETM-3D, on the benchmark domain $ρ\geq0$. Its homogeneous quadratic triad preserves Euclidean quadratic energy, while the full vector field has constant divergence -8. A shifted quadratic Lyapunov function yields an explicit positively invariant absorbing set and therefore global ultimate boundedness. The equilibria reduce generically to a scalar quartic, with a separate singular case at $ρ=\sqrt{5}$. Continuation reveals two saddle-node bifurcations, a generic transcritical bifurcation, a supercritical Hopf bifurcation, and a fold of limit cycles. Stable and unstable period-one branches meet at the fold with their nontrivial Floquet multipliers approaching +1. Just beyond this point, a 25-point logarithmic parameter study gives a laminar-length scaling exponent of -0.551 (95% confidence interval [-0.621,-0.481]), consistent with type-I intermittency. At larger $ρ$, a chaotic attractor coexists with an asymptotically stable equilibrium and periodic windows occur. Structural invariants are used to distinguish the flow from several classical quadratic chaotic families.

nlin.CD↗

arXiv2026

A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot

To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position control problem. Specifically, the dynamic growing mechanism is considered in more detail. In contrast to other self-organizing methods, DGFNC adds new rules more conservatively; hence the pruning mechanism is omitted. Instead, the adaptive strategy 'adapts' the control system to parameter variation. Furthermore, a sliding mode-based nonlinear controller ensures system stability. The resulting general control strategy aims to achieve faster response with less computation while maintaining overall stability. Finally, the 3PSP is chosen due to its complex dynamics and the utility of such approaches in modern industrial systems. Several simulations support the merits of the proposed DGFNC strategy as applied to the 3PSP robot.

eess.SY↗

arXiv2026

Enhanced-FQL($λ$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay

This paper introduces a fuzzy reinforcement learning framework, Enhanced-FQL($λ$), that integrates novel Fuzzified Eligibility Traces (FET) and Segmented Experience Replay (SER) into fuzzy Q-learning with the Fuzzified Bellman Equation (FBE) for continuous control. The proposed approach employs an interpretable fuzzy rule base instead of complex neural architectures, while maintaining competitive performance through two key innovations: a fuzzified Bellman equation with eligibility traces for stable multi-step credit assignment, and a memory-efficient segment-based experience replay mechanism for enhanced sample efficiency. Theoretical analysis proves the proposed method convergence under standard assumptions. On the Cart--Pole benchmark, Enhanced-FQL($λ$) improves sample efficiency and reduces variance relative to $n$-step fuzzy TD and fuzzy SARSA($λ$), while remaining competitive with the tested DDPG baseline. These results support the proposed framework as an interpretable and computationally compact alternative for moderate-scale continuous control problems.

cs.LG↗