Natural projections with the observer property have proved effective in reducing the computational complexity of nonblocking supervisory control design, and the state sizes of the resulting controllers. In this paper we present an algorithm to verify this property, or if necessary to achieve it. A natural projection is a special type of general causal reporter map; for the latter an algorithm is already known for verification and modification. This algorithm could be used to verify the observer property of a natural projection, but if the natural projection is not an observer the algorithm is not applicable to modify it to an observer. Also, while a general reporter map always admits a unique smallest refinement with the observer property, a natural projection does not. Indeed there may exist several minimal extensions to the original observable event set of a natural projection. We show that the problem of finding a minimal extension is NP-hard, but propose a polynomial-time algorithm that always finds an acceptable extension. While not guaranteed to be minimal, it is in practice often reasonably small.

Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin’s minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and then has higher fuel efficiency than a non-adaptive dynamic programming (DP)controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The AVF data structure enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.

The fuel economy of a hybrid electric vehicle(HEV) is determined by its energy management strategy (EMS), while the conventional EMS usually suffers from enormous computation loads when solving a nonlinear optimization problem. To resolve this issue, this paper presents a computationally efficient EMS with close-to-optimal performance using very limited computation resources. Relying on the optimal solutions by offline dynamic programming (DP), a constrained model predictive control (MPC) can quickly determine the engine on/off status and then the torque split problem is solved by a value-based Pontryagin’s minimum principle (PMP). Two measures are taken to further reduce the online computation cost: by surface fitting, the tabular value function is replaced by piecewise linear polynomials and thus the memory occupation is greatly reduced; and by model approximation, the nonlinear torque split problem becomes a quadratic programming one that can be more rapidly solved. The testing results from processor-in-the-loop (PIL) simulation indicate that the proposed EMS can generate a fuel efficiency close to the one by DP, but saves 70% onboard memory space and 30% CPU utilization compared with the benchmark EMS without taking the two measures.

Soft actuators have the advantages of compliance and adaptability when working with vulnerable objects, but the deformation shape of the soft actuators is difficult to measure or estimate. Soft sensors made of highly flexible and responsive materials are promising new approaches to the shape estimation of soft actuators, but suffer from highly nonlinear, hysteresis, and time-variant properties. A nonlinear and adaptive state observer is essential for the shape estimation from soft sensors. Current state estimation methods rely on complex nonlinear data-fitting models, and the robustness of the estimation methods is questionable. This study investigates the soft actuator dynamics and the soft sensor model as a stochastic process characterized by the Gaussian Process (GP) model. The unscented Kalman filter (UKF) is applied to the GP model for more reliable variance adjustment during the sequential state estimation process than conventional methods. In addition, a major limitation of the GP model is its computational complexity during online inference. To improve the real-time performance while guaranteeing accuracy, we introduce an edge server to decrease the onboard computational and memory overhead. The experiments showcase a significant improvement in estimation accuracy and real-time performance compared to baseline methods.

Modern smart assembly lines commonly include electric tools with built-in sensors to tighten safety-critical joints. These sensors generate data that are subsequently analyzed by human experts to diagnose potential tightening errors. Previous research aimed to automate the diagnosing process by developing diagnosing models based on tightening theory and calibration of the friction coefficient in specific lab setups. Generalizing these results is difficult and often unsuccessful since friction coefficients vary between lab and production environments. To overcome this problem, this paper presents a novel methodology that builds multi-label classification deep learning models for diagnosing tightening errors using production data. The proposed methodology comprises three key contributions, i.e., the Labrador method, the Model Combo (MoBo) framework, and a heuristic evaluation method. Labrador is an elastic deep learning based sensor fusion method that (1) uses feature encoders to extract features; (2) conducts data-level and/or feature-level sensor fusion in both time and frequency domains; and (3) performs multi-label classification to detect and diagnose tightening errors. MoBo is a configurable and modular framework that supports Labrador in identifying optimal feature encoders. With MoBo and Labrador, one can easily explore and design a bounded search space for sensor fusion strategies (SFSs) and feature encoders. In order to identify the optimal solution within the defined search space, this paper introduces a heuristic method. By evaluating the trade-off between machine learning (ML) metrics (e.g., accuracy, subset accuracy, and F1) and operational (OP) metrics (e.g., inference latency), the proposed method identifies the most suitable solution depending on the requirements of individual use cases. In the experimental evaluation, we adopt the proposed methodology to identify the most suitable multi-label classification solutions for diagnosing tightening errors. To optimize ML metrics, the identified solution achieved 99.69% accuracy, 93.39% subset accuracy, 97.39% F1, and 6.68ms inference latency. To optimize OP metrics, the identified solution achieved 99.66% accuracy, 92.65% subset accuracy, 97.28% F1, and 2.41ms inference latency.