Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

<p>In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.</p>

Assimilation and extension of particle image velocimetry data of turbulent Rayleigh–Bénard convection using direct numerical simulations

A novel method for assimilating and extending measured turbulent Rayleigh–Bénard convection data is presented, which relies on the fractional step method also used to solve the incompressible Navier–Stokes equation in direct numerical simulations. Our approach is used to make measured tomographic particle image velocimetry (tomo PIV) fields divergence-free and to extract temperature fields. Comparing the time average of the extracted temperature fields with the temporally averaged temperature field, measured using particle image thermometry in a subdomain of the flow geometry, shows that extracted fields correlate well with measured fields with a correlation coefficient of $$C_{T\tilde{T}}=0.84$$ C T T ~ = 0.84 . Additionally, extracted temperature fields as well as divergence-free velocity fields serve as initial fields for subsequent direct numerical simulations with and without feedback which generate small-scale turbulence initially absent in the experimental data. Although the tomo PIV data set was spatially under-resolved and did not include any information on the boundary layers, the here-proposed method successfully generates velocity and temperature fields featuring small-scale turbulence and thermal as well as kinetic boundary layers, without disturbing the large-scale circulation contained in the original experimental data significantly. The latter is underpinned by high vertical and horizontal velocity correlation coefficients—computed from velocity fields averaged in time and horizontal x-direction obtained from the measurement and from the simulation without feedback—of $$C_{v\tilde{v}}=0.92$$ C v v ~ = 0.92 and $$C_{w\tilde{w}}=0.91$$ C w w ~ = 0.91 representing the large-scale structure. For simulations with feedback, the generated velocity fields resemble the experimental data increasingly well for higher feedback gain values, whereas the temperature fluctuation intensity deviates noticeably from the values obtained from a direct numerical simulation without feedback for gain values $$\alpha \ge 1$$ α ≥ 1 . Thus, a feedback gain of $$\alpha =0.1$$ α = 0.1 was found optimal with correlation coefficients of $$C_{v\tilde{v}}=0.96$$ C v v ~ = 0.96 and $$C_{w\tilde{w}}=0.95$$ C w w ~ = 0.95 as well as a realistic temperature fluctuation intensity profile. The xt-averaged temperature fields obtained from the direct numerical simulations with and without feedback correlate somewhat less with the extracted temperature field ($$C_{T\tilde{T}}\approx 0.6$$ C T T ~ ≈ 0.6 ), which is presumably caused by spatially under-resolved and temporally oscillating initial tomo PIV fields reflected by the extracted temperature field. Graphical abstract

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

A power balance simulator to examine business continuity in hospital facilities due to power outages in a disaster

In hospitals, the energy supply is the key to ensuring modern medical care even during power outages due to a disaster. This study qualitatively examined whether the supply-demand balance can be stabilized by the private generator prepared by the hospital building during stand-alone operations under disaster conditions. In the nanogrid of the hospital building, the power quality was examined based on the AC frequency, which characterizes the supply-demand balance. Gas engine generators, emergency diesel generators, photovoltaic panels, and storage batteries were presumed to be the private generators in the hospital building. The output reference values for the emergency diesel and gas engine generators were set using droop control, and the C/D controller enabled synchronized operation. In addition, to keep the AC frequency fluctuation minor, the photovoltaic panels were designed to suppress the output fluctuation using storage batteries. As a result of case studies, the simulator predicts that the frequency fluctuation varies greatly depending on the weather conditions and the fluctuation suppression parameters, even for the same configuration with the same power generation capacity. Therefore, it is preferable to increase the moving average time of the output and reduce the feedback gain of the storage battery to suppress the output fluctuation from the photovoltaics. However, there is a tradeoff between suppressing the output fluctuation and the minimum required storage capacity. Furthermore, since the photovoltaics' output varies with the weather, other private generators' capacity and control parameters significantly impact power quality. The simulator proposed in this study makes it possible to study each hospital's desirable private generator configuration.

Robust Control for Non-Minimum Phase Systems with Actuator Faults: Application to Aircraft Longitudinal Flight Control

This study is concerned with developing a robust tracking control system that merges the optimal control theory with fractional-order-based control and the heuristic optimization algorithms into a single framework for the non-minimum phase pitch angle dynamics of Boeing 747 aircraft. The main control objective is to deal with the non-minimum phase nature of the aircraft pitching-up action, which is used to increase the altitude. The fractional-order integral controller (FIC) is implemented in the control loop as a pre-compensator to compensate for the non-minimum phase effect. Then, the linear quadratic regulator (LQR) is introduced as an optimal feedback controller to this augmented model ensuring the minimum phase to create an efficient, robust, and stable closed-loop control system. The control problem is formulated in a single objective optimization framework and solved for an optimal feedback gain together with pre-compensator parameters according to an error index and heuristic optimization constraints. The fractional-order integral pre-compensator is replaced by a fractional-order derivative pre-compensator in the proposed structure for comparison in terms of handling the non-minimum phase limitations, the magnitude of gain, phase-margin, and time-response specifications. To further verify the effectiveness of the proposed approach, the LQR-FIC controller is compared with the pole placement controller as a full-state feedback controller that has been successfully applied to control aircraft dynamics in terms of time and frequency domains. The performance, robustness, and internal stability characteristics of the proposed control strategy are validated by simulation studies carried out for flight conditions of fault-free, 50%, and 80% losses of actuator effectiveness.

Lévy noise-induced self-induced stochastic resonance in a memristive neuron

All previous studies on self-induced stochastic resonance (SISR) in neural systems have only considered the idealized Gaussian white noise. Moreover, these studies have ignored one electrophysiological aspect of the nerve cell: its memristive properties. In this paper, first, we show that in the excitable regime, the asymptotic matching of the deterministic timescale and mean escape timescale of an $$\alpha $$ α -stable Lévy process (with value increasing as a power $$\sigma ^{-\alpha }$$ σ - α of the noise amplitude $$\sigma $$ σ , unlike the mean escape timescale of a Gaussian process which increases as in Kramers’ law) can also induce a strong SISR. In addition, it is shown that the degree of SISR induced by Lévy noise is not always higher than that of Gaussian noise. Second, we show that, for both types of noises, the two memristive properties of the neuron have opposite effects on the degree of SISR: the stronger the feedback gain parameter that controls the modulation of the membrane potential with the magnetic flux and the weaker the feedback gain parameter that controls the saturation of the magnetic flux, the higher the degree of SISR. Finally, we show that, for both types of noises, the degree of SISR in the memristive neuron is always higher than in the non-memristive neuron. Our results could guide hardware implementations of neuromorphic silicon circuits operating in noisy regimes.

Output-Feedback Position Tracking Servo System with Feedback Gain Learning Mechanism via Order-Reduction Speed-Error-Stabilization Approach

This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of observers without system parameter information for estimating the position reference derivative and speed and acceleration errors and (b) an order reduction exponential speed error stabilizer via active damping injection to enable the application of a feedback-gain-learning position-tracking action. A hardware configuration using a QUBE-servo2 and myRIO-1900 experimentally validates the closed-loop improvement under various scenarios.

Robust attitude control of the three-dimensional unknown chaotic satellite system

This paper proposes a novel continuous-time robust direct adaptive controller for the attitude control of the three-dimensional unknown chaotic spacecraft system. It considers that the plant’s nonlinear terms, exogenous disturbances, and model uncertainties are unknown and bounded; the controller design is independent of the system’s nonlinear terms. These controller attributes flourish the robust performance of the closed-loop and establish smooth state vector convergence to zero. The proposed controller consists of three parts: (1) a linear controller establishes the stability of the closed-loop at the origin, (2) a nonlinear controller component that autonomously adjusts the feedback gain, and (3) a nonlinear adaptive controller compensates for the model uncertainties and external disturbances using the online estimates of bounds and model uncertainties. The output of this part remains within a given upper and lower bound. The feedback controller gain is large when the state variables are away from the origin and become small in the origin’s vicinity. This feature is novel and contributes to the synthesis of smooth control effort that establishes robust fast and oscillation-free convergence of the state variables to zero. The Lyapunov direct stability analysis assures the global asymptotic robust stability of the closed-loop. Computer simulations and comparative analysis are included to verify the theoretical findings.

Back to feedback: aberrant sensorimotor control in music performance under pressure

Precisely timed production of dexterous actions is often destabilized in anxiogenic situations. Previous studies demonstrated that cognitive functions such as attention and working memory as well as autonomic nervous functions are susceptible to psychological stress in skillful performance while playing sports or musical instruments. However, it is not known whether the degradation of sensorimotor functions underlies such a compromise of skillful performance due to psychophysiological distress. Here, we addressed this issue through a set of behavioral experiments. After artificially delaying the timing of tone production while playing the piano, the local tempo was abnormally disrupted only under pressure. The results suggest that psychological stress degraded the temporal stability of movement control due to an abnormal increase in feedback gain. A learning experiment further demonstrated that the temporal instability of auditory-motor control under pressure was alleviated after practicing piano while ignoring delayed auditory feedback but not after practicing while compensating for the delayed feedback. Together, these findings suggest an abnormal transition from feedforward to feedback control in expert piano performance with psychological stress, which can be mitigated through specialized sensorimotor training that involves piano practice while volitionally ignoring the artificially delayed provision of auditory feedback.