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Maximum unnoticeable added dynamics (MUAD) envelopes have been largely used to assess the adequacy of low-order equivalent systems (LOES) in handling qualities assessment and simulator validation. However, research has shown that more thorough verification of the adequacy of this method is required. This paper studies the influence of the baseline aircraft dynamics on the MUAD envelopes. The assessment is based on quantitative measures of pilot control behaviour obtained with the cybernetic approach. The measures were taken from a human-in-the-loop pitch tracking task experiment with compensatory display. The results are consistent with the MUAD envelopes and reveal no interaction effects of the baseline and added dynamics on the pilot control behaviour. This suggests that, under the considered conditions, the MUAD envelopes shall remain constant independently of the bandwidth of the baseline controlled dynamics.

Humans can rapidly change their low-frequency arm dynamics (i.e., stiffness) to resist forces or give way to them. Quantifying driver’s time-varying arm dynamics is important for the development of steer-by-wire systems and haptic driver support systems. Conventional LTI identification, and even time-varying techniques such as wavelets, fail to capture rapidly-varying low-frequency dynamics. In this study, we propose to estimate driver admittance in real-time, using grip force measurement of the hands on the steering wheel and linear parameter-varying (LPV) modeling techniques. We hypothesized that grip force is strongly correlated to neuromuscular admittance, and can serve as an appropriate scheduling variable for an LPV model. We performed an experiment in which 18 subjects performed a boundary tracking task, and applied torque perturbations to the steering wheel to perform a baseline LTI identification. Six different boundary widths were used to evoke changes in admittance, while their grip force was measured with pressure gloves. A global LPV model is identified by linear interpolation between the local LTI models identified for each boundary width. The estimated stiffness and damping parameters varied proportionally with the grip force. Although small between-subject variations in grip force levels are found, we conclude that grip force can indeed serve as an appropriate scheduling variable for a global LPV model, which is capable of tracking fast-changing admittance changes. Future work focuses on using the LPV model in realistic driving tasks, permitting admittance estimates to be obtained without the need to apply external disturbance torques on the steering wheel.

In this paper, the Linear Parameter Varying (LPV) model identification framework is applied to estimating time-varying human controller (HC) dynamics in a single-loop tracking task. Given the inherently unknown time changes in HC behavior, a global LPV approach with experimentally determined Scheduling Functions (SFs) is needed for this application. In this paper, a methodology based on the Predictor-Based Subspace Identification (PBSID) algorithm is tested. Using Monte Carlo simulation data matching a recent experimental study, two experimental SFs derived from measured HC control inputs are tested for their LPV model identification performance. The results are compared with LPV models obtained using the true (analytical) SFs used for generating the simulation data. An experimental SF obtained from the double derivative of HCs’ control inputs using zero-phase low-pass filtering was found to yield time-varying HC model estimates of equivalent accuracy as obtained with the analytical SFs; a promising result for future application of this methodology to measured HC behavior.

Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common state sequences are extracted from the model and clustered as driving patterns. The Next Generation SIMulation dataset on the I-80 highway is used for learning and evaluating. The experimental results demonstrate high accuracy of car-following model fitting.

Model-based dynamic optimization of the water-flooding process in oil reservoirs is a computationally complex problem and suffers from high levels of uncertainty. A traditional way of quantifying uncertainty in robust water-flooding optimization is by considering an ensemble of uncertain model realizations. These models are generally not validated with data and the resulting robust optimization strategies are mostly offline or open-loop. The main focus of this work is to develop an adaptive or online robust optimization scheme using residual analysis as a major ingredient. The models in an ensemble are confronted with data and an adapted ensemble is formed with only those models that are not invalidated. As a next step, the robust optimization is again performed (i.e., updated/adjusted) with this adapted ensemble. The adapted ensemble gives a less conservative description of uncertainty and also reduces the high computational cost involved in robust optimization. Simulation example shows that an increase in the objective function value with a reduction of uncertainty on these values is obtained with the developed adaptive robust scheme compared to an open-loop offline robust strategy with the full ensemble and an adaptive scheme using Ensemble Kalman Filter (EnKF), which is one of the most common parameter estimation methods in reservoir simulations.

Hydraulic driven manipulators face serious control problems due to the nonlinear system dynamics and model and parametric uncertainties of hydraulic actuators. In this paper, a novel sensor-based Incremental Nonlinear Dynamic Inversion controller is applied to force tracking control of hydraulic actuators of a hexapod flight simulator motion system, which together with an outer-loop motion tracking controller forms a motion control system. Due to the use of feedback of pressure difference derivatives, the proposed technique is not dependent on accurate model and parameters, which makes the controller inherently robust to model uncertainties. Furthermore, The sensor-based control approach is particularly suitable for hydraulic force tracking in existence of an outer-loop controller decoupling hydraulic-mechanic interaction term from the inner-loop dynamics. Simulation results indicate that the novel approach yields better tracking performance and confirm the greater robustness to model and parametric uncertainties compared with a traditional nonlinear dynamic invention approach.

Active power control can be used to balance the total power generated by wind farms with the power consumed on the electricity grid. With the increasing penetration levels of wind energy, there is an increasing need for this ancillary service. In this paper, we show that the tracking of a certain power reference signal provided by the transmission system operator can be significantly improved by using feedback control at the wind farm level. We propose a simple feedback control law that significantly improves the tracking behavior of the total power output of the farm, resulting in higher performance scores. The effectiveness of the proposed feedback controller is demonstrated using high-fidelity computational fluid dynamics simulations of a small wind farm.

In the present manuscript, we {\color{red} investigate and demonstrate the use of outer approximation methods} for simultaneously optimising the placement and operation of control valves in water distribution networks. The problem definition results in a mixed-integer nonlinear program with nonconvex constraints. We simplify the formulation, compared to previous literature, in order to reduce the degree of nonlinearity in the constraints and decrease the total problem size. We then {\color{red} formulate the application of} outer approximation based methods for the generation of good quality local optimal solutions for {\color{red} the considered co-design problem}. Finally, we present the results of applying the developed techniques to two case studies, and also comparing the performances of the outer approximation approaches with those of other local mixed integer nonlinear programming solution methods.

We consider the infinite-horizon optimal control of discrete-time, Lipschitz continuous piecewise affine systems with a single input. Stage costs are discounted, bounded, and use a 1 or ∞-norm. Rather than using the usual fixed-horizon approach from model-predictive control, we tailor an adaptive-horizon method called optimistic planning for continuous actions (OPC) to solve the piecewise affine control problem in receding horizon. The main advantage is the ability to solve problems requiring arbitrarily long horizons. Furthermore, we introduce a novel extension that provides guarantees on the closed-loop performance, by reusing data (“learning”) across different steps. This extension is general and works for a large class of nonlinear dynamics. In experiments with piecewise affine systems, OPC improves performance compared to a fixed-horizon approach, while the data-reuse approach yields further improvements.

This paper addresses the detection and isolation of replay attacks on sensor measurements. As opposed to previously proposed additive watermarking, we propose a multiplicative watermarking scheme, where each sensor's output is separately watermarked by being fed to a SISO watermark generator. Additionally, a set of equalizing filters is placed at the controller's side, which reconstructs the original output signals from the received watermarked data. We show that the proposed scheme has several advantages over existing approaches: it has no detrimental effects on the closed-loop performance in the absence of attacks; it can be designed in a modular fashion, independently of the design of the controller and anomaly detector; it facilitates the detection of replay attacks and the isolation of the time at which the replayed data was recorded. These properties are discussed in detail and the results are illustrated through a numerical example.

The problem considered is to construct all solutions of an equation for a tuple of languages. The tuple in synchronous composition should equal a considered language. Of special interest are the maximal solutions with respect to a partial order relation on the set of solutions. The motivation of the problem is coordination control. The approach to the problem is to transform the equation to a fixedpoint equation. An algorithm is proposed which constructs tuples of maximal solutions by convergence of a sequence of tuples to a solution of the fixed-point equation. Modular supervisory control is shown to benefit from the proposed approach.