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Keeping cognitive stress at a healthy range can improve the overall quality of life: helping subjects to decrease their high levels of arousal, which will make them relaxed, and elevate their low levels of arousal, which could increase their engagement. With recent advances in wearable technologies, collected skin conductance data provides us with valuable information regarding ones’ cognitive stress-related state. In this research, we aim to create a simulation environment to control a cognitive stress-related state in a closed-loop manner. Toward this goal, by analyzing the collected skin conductance data from different subjects, we model skin conductance response events as a function of simulated environmental stimuli associated with cognitive stress and relaxation. Then, we estimate the hidden stress-related state by employing Bayesian filtering. Finally, we design a fuzzy control structure to close the loop in the simulation environment. Particularly, we design two classes of controllers: (1) an inhibitory controller for reducing cognitive stress and (2) an excitatory controller for increasing cognitive stress. We extend our previous work by implementing the proposed approach on multiple subjects’ profiles. Final results confirm that our simulated skin conductance responses are in agreement with experimental data. In a simulation study based on experimental data, we illustrate the feasibility of designing both excitatory and inhibitory closed-loop wearable-machine interface architectures to regulate the estimated cognitive stress state. Due to the increased ubiquity of wearable devices capable of measuring cognitive stress-related variables, the proposed architecture is an initial step to treating cognitive disorders using non-invasive brain state decoding.

Within civil engineering, the inclusion of control strategies is confined to limited application areas due to the practical overhead and an alleged conservatism. This paper presents a simple yet flexible framework for experimental testing of closed-loop structural systems. Compared to alternative existing solutions, the proposed framework requires limited programming knowledge, which heavily simplifies the implementation for the operator and thus reduces the associated practical overhead. The feasibility of the framework is demonstrated through two experimental case studies in which control strategies are used to, respectively, design and implement a hybrid tuned mass damper (TMD) and conduct closed-loop model updating. The case studies exemplify the diversity of control applications within civil engineering, and it is noted that while hybrid TMDs are used in the civil engineering industry, limited experimental application studies exist on closed-loop model updating of structural systems. It is concluded that the presented framework can be applied to cover a variety of structural control applications within civil engineering and the successful implementation of the framework in the two case studies highlights the benefits of feedback control within these applications.

International audience; This work tackles the reachability problem of uncertain discrete-time linear systems controlled by estimated state feedback regulators. First an augmented system is considered in order to include the effect of the estimation error on the behavior of the closed loop system. Then, based on interval analysis, an interval predictor is proposed to compute tight trajectory tubes that contain in a guaranteed way the actual state trajectory of the augmented system. Moreover, under the standard controllability and observability assumptions of linear systems, the convergence of the width of these trajectory tubes is shown.

Autonomous optimization refers to the design of feedback controllers that steer a physical system to a steady state that solves a predefined, possibly constrained, optimization problem. As such, no exogenous control inputs such as setpoints or trajectories are required. Instead, these controllers are modeled after optimization algorithms that take the form of dynamical systems. The interconnection of this type of optimization dynamics with a physical system is however not guaranteed to be stable unless both dynamics act on sufficiently different timescales. In this paper, we quantify the required timescale separation and give prescriptions that can be directly used in the design of this type of feedback controllers. Using ideas from singular perturbation analysis, we derive stability bounds for different feedback laws that are based on common continuous-time optimization schemes. In particular, we consider gradient descent and its variations, including projected gradient, and Newton gradient. We further give stability bounds for momentum methods and saddle-point flows. Finally, we discuss how optimization algorithms like subgradient and accelerated gradient descent, while well-behaved in offline settings, are unsuitable for autonomous optimization due to their general lack of robustness., IEEE Transactions on Automatic Control, 66 (2), ISSN:0018-9286, ISSN:1558-2523

This paper reports human pointing behaviors in vision-based pointing interface system, to make a mathematical model of them for designing easy-to-use interface. In natural pointing situations, we point targets at distant position with various postures, for example straight arm style or bent elbow style. We analyze their difference in pointing behaviors with assuming the pointing interface system as a feedback control model including an indicator. The difference had been confirmed in the step responses and the estimated parameters in the transfer functions, and matches to our actual experiences in those pointing styles. The estimation accuracy of indicated position from indicator's posture in the intermediate styles has been also analyzed. The results said that the reference point of indication smoothly moves from indicator's eye to his or her elbow according to the elbow joint angle., 13th IFAC Symposium on Analysis, Design, and Evaluation ofHuman-Machine Systems HMS 2016: Kyoto, Japan, 30 August--2 September 2016

In this paper, a controller design methodology is proposed for a CLLLC -type bidirectional resonant converter. The soft switching of all devices in this topology and the very high operating frequency lead to increased overall system efficiency. However, the dynamic nature of this converter is highly dependent on loading conditions, which proves challenging when designing the voltage and current closed-loop controllers. System instability is mainly due to the high-Q resonant peaking, which is observed in the open-loop bode-plots. In this paper, the controller design methodology is proposed, which accounts for the dynamics behavior due to load variations. The controller stability will be evaluated against the entire range of operating switching frequency. Both battery charging and regeneration modes will be described and analyzed. The focal contribution of this paper will focus on defining the worst operating scenarios for the converter using system-level modeling and analysis. In addition, the controller will be defined based on these operating points. To validate and verify the controller design methodology proposed, a 3.5-kW converter is designed with the appropriate output voltage and current loop controllers. The step response verified a stable system designed and thus proving the proposed controller design methodology.

This paper investigates the closed-loop dynamics of the Tapping Mode Atomic Force Microscopy using a new mathematical model based on the averaging method in Cartesian coordinates. Experimental and numerical observations show that the emergence of chaos in conventional tapping mode AFM strictly limits the imaging speed. We show that, if the controller of AFM is tuned to be faster than a certain threshold, the closed-loop system exhibits a chaotic behavior. The presence of chaos in the closed-loop dynamics is confirmed via bifurcation diagrams, Poincaré sections, and Lyapunov exponents. Unlike the previously detected chaos due to attractive forces in the AFM, which can be circumvented via simple changes in operation parameters, this newly identified chaos is seemingly inevitable and imposes an upper limit for the closed-loop bandwidth of the AFM.

International audience; Phase noise is a clue performance indicator for MEMS-based resonant sensors. The optimal resolution achievable with these sensors is limited by the close-to-the-carrier phase noise resulting from the modulation of noise sources by the mechanical resonator. In this paper, we focus on the effect of a white noise input source on a one-sided capacitive MEMS resonator. We study how the closed-loop phase shift affects its frequency stability. Our study reveals the existence of optimal points, with phase noise minimization and SNR maximization, which cannot be predicted by a traditional third-order Taylor-Series approach. We reveal that, due to the modulation of the actuation force by the electrostatic nonlinearity, tuning the closed-loop phase shift to improve the oscillator frequency stability is especially relevant.

A simple method for the automatic tuning of PID controllers in closed loop is proposed. A limit cycle is generated through a nonlinear feedback path from the process output to the controller reference signal. The frequency of this oscillation is above the crossover frequency and below the critical frequency of the loop transfer function. The amplitude and frequency of the oscillation are estimated and the control parameters are adjusted iteratively such that the closed loop transfer function from the controller reference to the process output attains a specified amplitude at the oscillation frequency.

In this paper, we present a sliding mode control algorithm to robustly stabilize a class of underactuated mechanical systems that are not linearly controllable and violate Brockett's necessary condition for smooth asymptotic stabilization of the equilibrium, with parametric uncertainties. In defining the class of systems, a few simplifying assumptions are made on the structure of the dynamics; in particular, the damping forces are assumed to be linear in velocities. We first propose a switching surface design for this class of systems, and subsequently, a switched algorithm to reach this surface in finite time using conventional and higher order sliding mode controllers. The stability of the closed-loop system is investigated with an undefined relative degree of the sliding functions. The controller gains are designed such that the controller stabilizes the actual system with parametric uncertainty. The proposed control algorithm is applied to two benchmark problems: a mobile robot and an underactuated underwater vehicle. Simulation results are presented to validate the proposed scheme. � 2009 IEEE.

An extremum controller based on a pulse technique is examined. Plants consisting of the cascade of a piecewise linear static function and a LTI system are considered. Numerical methods for stability and performance analysis of the closed-loop systems are derived. Performance is measured by the ability to track a time-varying optimal point. An illustrative example where it is desirable to control the process to a saturation instead of an extremum is finally presented.

This paper concerns the filtering problem for a class of stochastic nonlinear systems where the drift term may depend either on some external function (open-loop system) or on the system output ( closed-loop system), through a controller. Such systems are denoted feedback systems. The following result is proven: for feedback systems, the optimal filter in the open-loop case remains optimal when the feedback is closed. The proof is obtained by showing equivalence of suitable expressions for the estimators of the open-loop and closed-loop systems, obtained using the Kallianpur--Striebel formula [G. Kallianpur and C. Striebel, Ann. Math. Statist., 39 (1968), pp. 785--801].

Cataloged from PDF version of article. Robust adaptive sampled-data control of a class of linear systems under structured perturbations is considered. The controller is a time-varying state-feedback law having a fixed structure, containing an adjustable parameter, and operating on sampled values. The sampling period and the controller parameter are adjusted with simple adaptation rules. The resulting closed-loop system is shown to be stable for a class of unknown perturbations. The same result is also shown to be applicable to decentralized control of interconnected system.

In this paper the pole assignment with constant gain output feedback is studied for a SIMO System. A reduced order system with the set of unassigned poles satisfying the constraint conditions, obtained by removing the polynomial with the set of assigned poles, simplifies the design procedure. The root locus method is adopted for the proper fixation of the feedback gain.