Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.Comment: 7 pages, 2 figures. Accepted at the 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS 202

Structural and chemical alteration of glauconite under progressive acid treatment

Boiling glauconite from the El-Gideda area of Egypt in different concentrations of HCl and HSO for different periods led to a modified structure. Treatment resulted in progressive destruction of the structure, leaving X-ray amorphous silica and only relics of the original mineral. The glauconitic material was modified structurally in order to increase its adsorption activity. The glauconite was evaluated in terms of mineralogy, chemistry, morphology, structural modification, octahedral cation leaching rate, surface area and cation exchange capacity using X-ray diffraction, infrared spectroscopy, X-ray fluorescence, scanning electron microscopy and surface area analysis. The ratio of extracted octahedral cations to the total octahedral cations in the untreated glauconite was taken as a measure of octahedral sheet decomposition. A progressive decrease in crystallinity and the formation of X-ray amorphous silica Si-O vibration bands at 1100, 800 and 494 cm accompanied octahedral cation depletion. Acid activation using 2 M and 4 M HCl for 6 h destroyed 30% and 61% of the octahedral sheet, respectively. In contrast, similar treatment using 2.9 and 5.5 M HSO destroyed 48% and 93% of the octahedral sheet, respectively. Depending on the extent of cation depletion, the 4 M HCl product surface areas were as high as 259 m/g, whereas the surface area of the 5.5 M HSO product was only 63 m/g. The progressive increase in surface area was due to glauconite morphology alteration. Acid-induced dissolution of Al, Fe, Mg cations from octahedral sheet edges led to a wedge-like splitting of the glauconite crystals, mesopore creation, and greater access to interlayer galleries

Variational inference for predictive and reactive controllers

Active inference is a general framework for decision-making prominent neuroscience that utilizes variational inference. Recent work in robotics adopted this framework for control and state-estimation; however, these approaches provide a form of ‘reactive’ control which fails to track fast-moving reference trajectories. In this work, we present a variational inference predictive controller. Given a reference trajectory, the controller uses its forward dynamic model to predict future states and chooses appropriate actions. Furthermore, we highlight the limitation of the reactive controller such as the dependency between estimation and control

Adaptive Manipulator Control using Active Inference with Precision Learning

Active inference provides a framework for decisionmaking where the optimization is achieved by minimizing freeenergy. Previous work has used this framework for control and state-estimation of a robotic manipulator. This required manual definition of precision matrices which serve as controller gains. This paper provides an implementation for control and state-estimation where the precision matrices are tuned during execution-time (precision learning). Learning the precision matrices means automatically adjusting the controller’s gains which decreases oscillations and overshoot

Beta residuals: improving fault-tolerant control for sensory faults via bayesian inference and precision learning

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches

Towards stochastic fault-tolerant control using precision learning and active inference

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed

Active inference for fault tolerant control of robot manipulators with sensory faults

We present a fault tolerant control scheme for robot manipulators based on active inference. The proposed solution makes use of the sensory prediction errors in the free-energy to simplify the residuals and thresholds generation for fault detection and isolation and does not require additional controllers for fault recovery. Results validating the benefits in a simulated 2DOF manipulator are presented and the limitations of the current approach are highlighted