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This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. [ABSTRACT FROM AUTHOR]

The removal of hexavalent chromium [Cr (VI)] using non-living cells of Yarrowia lipolytica was investigated. Batch and continuous studies on removal of Cr (VI) achieved 97% and 99% removal from aqueous phase, respectively. The specific uptake values at pH of 2 in batch process were 40.73 ± 1.3 mg/g and 30.09 ± 0.23 mg/g on non-living cells, when 100 and 200 mg/L of metal Cr (VI) concentrations were used. In order to investigate the regulation of Cr (VI) under continuous operation based on reaction volume numerically a new class of feedback controller from structure polynomial was designed. The proposed methodology was used to an experimentally kinetic model for a removal Cr (VI) from Yarrowia lipolytica biomass was showed satisfactory closed-loop performance the proposed controller. Starting from an off-line optimization performed in simulation, we present the controller implementation, focussing on the methodology required to could be suitable for implementation in real time. In our experimental results, we highlight some discrepancies between simulation and reality despite these differences, the controller managed to perform convergence to removal Cr (VI). Finally, the results validated with off-line samples suggest that the proposed control could be suitable for in application in potential scenarios for wastewater treatment. [ABSTRACT FROM AUTHOR]

Summary: In this article, the asymptotic synchronization of a class of fuzzy inertial neural networks (FINNs) with time‐varying delays is investigated. First, the direct analysis approach is applied to replace the accustomed variable transformation and the reduced‐order method for addressing the inertial term. Second, a suitable Lyapunov function and control scheme are devised to obtain several new sufficient conditions to guarantee the asymptotic synchronization of the class of FINNs with time‐varying delays. It turns out that the obtained criteria are simpler and more effective. Meanwhile, some numerical examples demonstrate the effectiveness of the proposed strategies and verify the theoretical results. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We explored the prescribed-time stability (PTSt) of impulsive piecewise smooth differential systems (IPSDS) based on the Lyapunov theory and set-valued analysis technology, allowing flexibility in selecting the settling time as desired. Furthermore, by developing a feedback controller, we employed the theoretical results to evaluate the synchronization behavior of impulsive piecewise-smooth network systems (IPSNS) within a prescribed time frame and obtained novel criteria to guarantee the synchronization objective. A numerical example was presented to validate the accuracy of the results. [ABSTRACT FROM AUTHOR]

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle nonlinear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization. [ABSTRACT FROM AUTHOR]

In this study, we proposed a novel active magnetic levitation (maglev) system to realize next-generation maglev transportation. The main feature of the proposed maglev system does not require magnetic guidance on guideway. The system can realize noncontact levitation without any counterforce from the guideway. The electromagnetic actuator attached to the system generates a magnetic force to maintain a constant distance between the system and the guideway (steel rail). Servo system employs position feedback controllers based on the distance. The feedback controllers are designed frequency responses from the magnetic force to the distance, and these can compensate for dependency of the operating point due to the nonlinear characteristics. Compared with previous systems that required counterforces, the servo system for the actuator can stabilize levitation in five directions. The proposed system was developed and demonstrated, showing stable movement along the guideway without counterforces. This eliminates the need for expensive permanent magnets or superconductors on the guideway. This offers significant practical benefits in terms of cost reduction and introduces a novel servo system.

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform.

The current paper deals with synchronization problem among chaotic nonlinear fractional order systems considering input saturation constraint. To develop the idea, at first a generalized sector condition and a memory-less nonlinear function are employed to deal with the saturation problem. Then a new state feedback controller is designed to achieve synchronization in master-slave chaotic fractional order nonlinear systems with input saturation. Using the state feedback controller, the asymptotic stability of whole dynamic error model between master and slave is achieved. The stability of the closed-loop system is guaranteed using Lyapunov theory and sufficient stability conditions are formulated in terms of caputo fractional derivative of a quadratic Lyapunov function and Linear Matrix Inequalities (LMI). Finally, to verify the effectiveness of the proposed control scheme, some simulation results are employed to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a delayed feedback control strategy for a fractional ecological infectious disease system with infected predators. Firstly, for the uncontrolled system, the stability of each equilibrium is discussed. Choosing the gestational delay as the branching parameter, the conditions of Hopf bifurcation are given at the positive equilibrium. The results show that the stable domain for the fractional system is larger than that for the integer-order system. Secondly, for the controlled system, the effects of gestational delay and feedback delay on the induction of the Hopf bifurcation are investigated, respectively. In addition, we find that the feedback gain coefficient also changes the critical value at which Hopf bifurcation occurs. Selecting a suitable feedback controller can effectively extend the stable domain of the system compared to the control-free system. Finally, the accuracy and effectiveness of the delay feedback controller in controlling the bifurcation are verified through numerical simulations.

We show that the hyperbolic modiffication of FitzHugh-Nagumo system de-ffined in a bounded domain G - Rn is global exponetally stablizable by a feedback controller action in an open subdomian ! - G such that G n ! is sufficiently thin. [ABSTRACT FROM AUTHOR]

This paper excogitates a bifurcation control strategy for a delayed fractional-order population dynamics model with incommensurate orders. First and foremost, by using stability theory of fractional differential equations, the sufficient conditions for the stability of the positive equilibrium are established. It is not difficult to find that the fractional-order system has a wider stability region than the traditional integer-order system. Second, taking time delay as bifurcation parameter, the sufficient criteria for Hopf bifurcation are obtained. In the next place, it is interesting to introduce a delayed feedback controller to guide Hopf bifurcation. The results reveal that the bifurcation dynamics of the model could be effectively controlled as long as the delay or fractional order is carefully adjusted. In conclusion, numerical simulations are carried out to confirm our theoretical results. [ABSTRACT FROM AUTHOR]

A novel robust Kalman filter (KF)-based controller design approach is proposed to accurately track a specified trajectory under unknown stochastic disturbance, known deterministic disturbance, and measurement noise. The system is nonlinear and is approximated by a piecewise-linear dynamic model. The Box–Jenkins model is an augmented model of the signal and the disturbance, is non-controllable and observable, while the signal model is controllable and observable. An emulator-based two-stage identification is employed to obtain an accurate system model needed to design the robust controller. The system and its KF are identified, and the signal and output errors are estimated. From the identified models, the signal, its KF, the disturbance model, and the whitening filter are all obtained using balanced model reduction techniques. It is shown that the signal model is a transfer matrix description relating the system output and KF residual, and the residual is the whitened output error. The disturbance model is identified by inverse filtering. A new combined feedforward–feedback controller is designed and implemented using an internal model of the reference driven by both the error between the reference and the signal estimate, and by the feedforwarded reference signal. The proposed scheme was successfully evaluated on a simulated autonomously guided drone. [ABSTRACT FROM AUTHOR]

In this work we design an explicit feedback controller u = Φ(ρ) for the exact controllability problem to the nonlinear Fokker-Planck equation ρt - Δβ(ρ) + div(uρ) = 0 in (0, T) x O, ρ(0) = ρ0, ρ(T) = ρ1, with reflecting boundary conditions (∇β(ρ) - uρ) ⋅ n = 0 on ∂O. Here, O ⊂ Rd, 1 ≤ d, is a bounded, open set with smooth boundary ∂O and β is a smooth monotonically increasing function. If ρ0 and ρ1 are probability densities, one obtains in particular the exact controllability for McKean-Vlasov stochastic differential equations on O with reflecting (impenetrable) barrier. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the practical stabilization of non-autonomous infinite-dimensional systems depending on a parameter. By the Lyapunov method, several sufficient conditions are provided to obtain the practical stabilization of two classes of non-autonomous semilinear evolution equations in Hilbert spaces. We propose some classes of memoryless state linear and nonlinear feedback controllers. Numerical examples are given to illustrate the validity of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the fixed-time synchronization of fractional-order Hopfield neural networks (FHNNs). The aim of this paper is to design a state-feedback controller to make the synchronization error convergent to zero within bounded time. Based on the Lyapunov function and fractional calculus theory, we derived some criteria of synchronization for delay-free FHNNs and delayed FHNNs, respectively. At the same time, the upper bound of settling time for synchronization are given. Numerical simulations demonstrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper further develops a relaxed method to reduce conservatism in H ∞ feedback control for continuous-time T-S fuzzy systems via a generalized non-quadratic Lyapunov function. Different from the results of some exisiting works, the generalized H ∞ state feedback controller is designed. The relaxed stabilization conditions are obtained by applying Finsler's lemma with the homogenous polynomial multipliers, and the H ∞ performance is acquired by solving an optimization problem. In addition, the proposed method could be expanded to handle other control problems for fuzzy systems. Two examples are given to show the validity of the proposed results. [ABSTRACT FROM AUTHOR]

An unreasonable allocation of resources has led to a low rate of output in the industry–university–research collaboration network. A solution to this problem is to control and predict the input and output. However, the network has the characteristics of strong nonlinearity and insufficient samples. It is difficult for the existing control methods to migrate to collaboration networks because the traditional control methods, including Proportional–Integral–Derivative (PID) control and Model Predictive Control (MPC), are usually not applied to the system with strong nonlinearity and the controlled system needs to have specific parameters, while the modern control methods, including feedforward control and feedback control, have their limitations in both parameters and other aspects. In addition, there is a lack of research on the control and output prediction of collaboration networks, and there is no effective and applicable scheme for the control and prediction. Considering the nonlinearity and insufficient samples of the collaboration network, a Feedforward Control–Feedback Control Model based on the Multi-Layer Perceptron (FCFCM-MLP) is proposed in this paper. Adopting the controller structure of the Grid Search-Multilayer Perceptron (GS-MLP), a control block diagram, a feedforward controller, a feedback controller, and prediction methods such as Harris Hawk Optimization-Support Vector Regression (HHO-SVR) are designed for the FCFCM-MLP, which effectively realizes the feedforward control, feedback control, and prediction of inputs and outputs. In this paper, simulation tests on output-feedback tracking control are conducted with real statistics of papers jointly produced by the industry–university–research collaboration network in the construction industry. The results show that the proposed model has obvious effectiveness. Specifically, compared with the model composed of other controller structures and prediction methods, the optimal model Particle Dynamic Multiple Perturbation_Butterfly Optimization Algorithm-Support Vector Regression_Grid Search-Multi-Layer Perceptron (PDM_BOA-SVR_GS-MLP) obtained in this paper can minimize the predictive control error and effectively improve the control accuracy. [ABSTRACT FROM AUTHOR]

Robust stable feedback (FB) controller design against plant perturbation is crucial for industrial servo systems. However, since the controller parameter design generally requires expert skills and/or considerable labor by engineers, an autonomous controller design technology would be promising. This paper presents an efficient autonomous design method that optimizes the parameters of a cascade structure FB controller to achieve robust stabilization. Conventionally, robust stabilization is realized by imposing stability constraints on all the perturbed plant model sets in a parameter optimization problem. As a result, the design time would be much longer owing to the complicated optimization problem. The proposed method makes the parameter optimization considering robust stability more efficient than the conventional method, by simplifying the stability constraint definition and optimizing the specified circle radius for the stability constraint based on a bisection method. The effectiveness of the proposed method is evaluated through an example FB controller design for a laboratory galvanometer scanner. [ABSTRACT FROM AUTHOR]

In this work, a freeway traffic control scheme is proposed to regulate traffic in freeway networks via multi‐class ramp metering. Specifically, each controlled on‐ramp of the system adopts a feedback regulator of proportional‐integral type and is able to compute the traffic flows entering the freeway mainstream, differentiated for each class of vehicles, e.g., cars and trucks. Since a crucial aspect of proportional‐integral controllers is the tuning of parameters, in this work an optimization‐based procedure is devised to determine the controller parameters to be used in the different on‐ramps of the network, according to the detected traffic conditions. To this end, a set of representative traffic scenarios is defined and, for each scenario, an offline optimization procedure is applied to determine the optimal parameters. Then, in real time, a controller parameter selector identifies the scenario associated with the current system conditions and communicates to the local controllers the optimal parameters to be applied. The effectiveness of the proposed control scheme is tested in a simulative example and the main results are discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper mainly studies the parameter estimation of fractional‐order uncertain coupling recurrent neural networks(FUCRNNs) in finite time. We construct a feedback controller and update rules to realize the synchronization and parameters identification of the proposed system in finite time. By designing a new Lyapunov function, a simple sufficient condition to ensure successful identification of parameters is derived. In addition, we also get the identification time and analyze the relationship between model parameters and settling time. Two numerical simulations demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Synchronization of chaotic dynamical systems with fractional order is receiving great attention in recent literature because of its applications in a variety of fields including optics, secure communications of analog and digital signals, and cryptographic systems. In this paper, chaos control of a new financial system, and chaos synchronization between two identical financial systems, and non-identical financial systems with integer and fractional order are investigated. Chaos control is based on a linear feedback controller for stabilizing chaos to unstable equilibrium. In addition, chaos synchronization, not only between two identical new chaotic financial systems, but also between the new financial system and an another financial system given in the literature is realized by using active control technique. The synchronization is done for integer and fractional order in each case. It is shown that chaotic behavior can be controlled easily to any unstable equilibrium point of the new financial system. Also, it is observed that synchronization is enhanced when the fractional order increases and approximates to one. Numerical simulations are used to verify the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents the analytical and numerical investigation on the global synchronization and anti-synchronization for a class of drive-response systems of fractional-order complex-valued gene regulatory networks with time-varying delays (DFGRNs). In our design, two kinds of adaptive feedback controllers are used to synchronize and anti-synchronize the proposed drive-response systems, and some sufficient conditions on the global asymptotical synchronization and anti-synchronization are given with the methods of the fractional Lyapunov-like functions and the fractional-order inequalities. In the numerical simulations, two minimum “estimated time”, T1 and T2, are computed to achieve the synchronization and anti-synchronization. We find that T1 and T2 increase with the decreasing of the fractional order of DFGRNs.

This paper further develops a relaxed method to reduce conservatism in H∞ feedback control for continuous-time T-S fuzzy systems via a generalized non-quadratic Lyapunov function. Different from the results of some exisiting works, the generalized H∞ state feedback controller is designed. The relaxed stabilization conditions are obtained by applying Finsler’s lemma with the homogenous polynomial multipliers, and the H∞ performance is acquired by solving an optimization problem. In addition, the proposed method could be expanded to handle other control problems for fuzzy systems. Two examples are given to show the validity of the proposed results.

In the current article, agreeing on the stability theory of fractional order systems, the conditions for the asymptotic stability of nonlinear fractional order systems are presented. We discuss the stabilization of new fractional order chaotic system where the state equations contain the same or different fractional orders. Then we design a control strategy for stabilization of new fractional order chaotic system. Our aim is to stabilize the unstable equilibrium points of fractional order chaotic systems by planning a fit linear state feedback controller. In the following, a comparison between linear state feedback controller and sliding mode control for commensurate and incommensurate new fractional order chaotic systems is conducted. Numerical simulations approve the validity of the presented results. [ABSTRACT FROM AUTHOR]

For high-fidelity quantum operations in ion traps, it is important to maintain the secular frequency of the trapped ions at a constant value. The radial secular frequency is proportional to the amplitude of the radio frequency (RF) signal applied to ion traps. Owing to the changes in the ambient temperature of a helical resonator and the minute vibration of the optical table, the amplitude can vary. Recently, a method for reducing the fluctuation in the RF signal amplitude, using a commercial universal proportional-plus-integral (PI) controller, has been introduced, which, in turn, reduces the secular frequency drift of the trapped ions. The method improves the capability to maintain the secular frequency at a constant value. However, the structure of the controller is fixed; thus, the control method cannot be changed to suit different experimental conditions, and the different feedback configuration cannot be implemented to increase the resolution. In this paper, we develop a field-programmable gate array (FPGA)-based feedback controller that allows the implementation of various automatic control methods and feedback configurations. In our experiments, the fluctuation in the amplitude of the RF signal was 1.806% using a commercial universal PI controller. The fluctuation was reduced to 0.099% using the developed FPGA-based PI controller, and to 0.102% using the developed FPGA-based lag compensator. By employing the developed FPGA control method, many other automating control methods can be applied to achieve a stable and high-performance control of the secular frequency. [ABSTRACT FROM AUTHOR]

One proves the exact controllability of the Fokker--Planck equation pt -- 1/2 Δ(up} -- div(bp) = 0,p(0) = ρ0,p(T) = p1,on aboundeddomain O with reflecting conditions on the boundary O. In particular, the exact controllability of stochastic differential equations with reflection to O is derived, and it is achieved by a nonlinear multivalued feedbac k controller u = Φ(., p), which steers p0 in p1 in a sufficiently large time T. [ABSTRACT FROM AUTHOR]

• A methane bioconversion model is constructed as an unstructured mechanistic model. • An observability and controllability analysis are executed with empirical Gramians. • The control scheme estimates non-measured states by an extended Kalman filter. • An LQR controller with integral feedback is designed to enable reference tracking. Methane is currently an emerging alternative feedstock for biological processes. In this paper, we study a particular methane to lactate bioconversion process based on the bacterium Methylomicrobium buryatense 5GB1, with the aim of designing suitable state estimators and controllers for the process. First, a nonlinear unsegregated, unstructured model, consisting of six dynamic mass flow balance equations and six state variables, is constructed and implemented as a dynamic simulator in MATLAB and Simulink. Simulation results match qualitatively with experimental data from literature. Second, an observability analysis is performed with the aim of finding suitable sensor configurations for state estimation. For each of the resulting observable configurations a linear Kalman filter is constructed, of which the performance is evaluated. This performance is not satisfactory and, therefore, extended and unscented Kalman filters are designed to overcome the nonlinear nature of the system. Third, a controllability analysis is executed to establish controllability with respect to the process inputs. Both the observability and controllability give an assessment of practical observability and controllability by using empirical Gramians. A linear quadratic regulator (LQR) with integral feedback is constructed to control the biomass and lactate concentrations. The results for a combined observer and controller scheme are promising and give reliable simulation results. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the boundedness and the input‐to‐state stabilisation problems are investigated for a kind of switched delayed differential systems with exogenous disturbances. To reduce the communication burden, an event‐triggered strategy is adopted in the feedback control loop to stabilise the addressed switched delayed differential systems. The notions of boundedness and input‐to‐state practical stability are employed to characterise the control objectives in the presence of exogenous disturbances, switching signals and event‐triggered schemes. Some upper bounds on the system states are obtained and the Zeno phenomenon is shown to be excluded under the proposed event‐triggered scheme. The gain of the feedback controller, the parameters of the event‐triggering function and the switching signals are jointly designed for the underlying delayed differential systems. At last, a numerical example is exploited to given to demonstrate the effectiveness of the authors' developed control scheme. [ABSTRACT FROM AUTHOR]

This study presents the design and experimental validation of feedback control along with a high‐gain observer to regulate the DC‐link voltage in dual‐stage grid‐tied photovoltaic (PV) system. In particular, the composite controller is designed to ensure accurate and fast control of the DC‐link voltage in response to abrupt changes in either the delivered active power or grid voltages. The high‐gain observer is employed to estimate the unknown disturbance, which is then cancelled by the feedback controller to ensure asymptotic regulation. Fast disturbance estimation is required to retain the nominal transient response specified under the feedback controller, which mandates using high‐observer gain. However, high‐observer gain magnifies the effect of the measurement noise, which results in poor steady‐state performances. This problem is addressed here by using a non‐linear gain to allow for on‐line adjustment of the observer gain. Specifically, a large observer gain is used during transients to ensure fast disturbance estimation, while a reduced observer gain is employed in the steady‐state regime to mitigate the effect of the measurement noise. Experimental results demonstrated the ability of the proposed controller to achieve accurate control, good transient response, fast disturbance rejection, and negligible sensitivity to the measurement noise. [ABSTRACT FROM AUTHOR]

Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n = 35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counterclockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials, we measured overall learning, which was later decomposed into a fast and slow process. We found that the longlatency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50–100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller. [ABSTRACT FROM AUTHOR]

We prove the existence of an optimal feedback controller for a stochastic optimization problem constituted by a variation of the Heston model, where a stochastic input process is added in order to minimize a given performance criterion. The stochastic feedback controller is found by solving a nonlinear backward parabolic equation for which one proves the existence and uniqueness of a martingale solution. [ABSTRACT FROM AUTHOR]

The optimal control problem governed by the stochastic reflection problem associated with a closed convex set K in Rd is reduced via the corresponding Kolmogorov equation to a deterministic bilinear parabolic optimal control problem on (0, T)Ã--K. In this way, one gets directly a stochastic optimal feedback controller by avoiding the standard dynamic programming equation associated with the stochastic optimal control problem. [ABSTRACT FROM AUTHOR]

Studies have shown that fractional calculus can describe and characterize a practical system satisfactorily. Therefore, the stabilization of fractional-order systems is of great significance. The asymptotic stabilization problem of delayed linear fractional-order systems (DLFS) subject to state and control constraints is studied in this article. Firstly, the existence conditions for feedback controllers of DLFS subject to both state and control constraints are given. Furthermore, a sufficient condition for invariance of polyhedron set is established by using invariant set theory. A new Lyapunov function is constructed on the basis of the constraints, and some sufficient conditions for the asymptotic stability of DLFS are obtained. Then, the feedback controller and the corresponding solution algorithms are given to ensure the asymptotic stability under state and control input constraints. The proposed solution algorithm transforms the asymptotic stabilization problem into a linear/nonlinear programming (LP/NP) problem which is easy to solve from the perspective of computation. Finally, three numerical examples are offered to illustrate the effectiveness of the proposed method.