Experimental validation off-line a nonlinear controller for removal of chromium using non-living cells of Yarrowia lipolytica.

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.

Optic flow enrichment via Drosophila head and retina motions to support inflight position regulation.

Developing a functional description of the neural control circuits and visual feedback paths underlying insect flight behaviors is an active research area. Feedback controllers incorporating engineering models of the insect visual system outputs have described some flight behaviors, yet they do not explain how insects are able to stabilize their body position relative to nearby targets such as neighbors or forage sources, especially in challenging environments in which optic flow is poor. The insect experimental community is simultaneously recording a growing library of in-flight head and eye motions that may be linked to increased perception. This study develops a quantitative model of the optic flow experienced by a flying insect or robot during head yawing rotations (distinct from lateral peering motions in previous work) with a single other target in view. This study then applies a model of insect visuomotor feedback to show via analysis and simulation of five species that these head motions sufficiently enrich the optic flow and that the output feedback can provide relative position regulation relative to the single target (asymptotic stability). In the simplifying case of pure rotation relative to the body, theoretical analysis provides a stronger stability guarantee. The results are shown to be robust to anatomical neck angle limits and body vibrations, persist with more detailed Drosophila lateral-directional flight dynamics simulations, and generalize to recent retinal motion studies. Together, these results suggest that the optic flow enrichment provided by head or pseudopupil rotation could be used in an insect's neural processing circuit to enable position regulation.

Time course of changes in the long-latency feedback response parallels the fast process of short-term motor adaptation.

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 long-latency 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.NEW & NOTEWORTHY We investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long-latency component of the feedback stretch response was upregulated in the early stages of learning and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.

Dynamics analysis of permanent magnet synchronous motor speed control with enhanced state feedback controller using a linear quadratic regulator.

This paper presents a robust speed regulation and control of a permanent magnet synchronous motor (PMSM). A linear quadratic regulator (LQR) based state feedback controller was developed to achieve a successful suppression of periodic disturbance of speed and torque. Sliding mode observer in conjunction with the disturbance observer was deployed in the control of motor speed. Simulations were carried out based on two compared controllers such as the state feedback controller and the conventional proportional-integral-derivative (PID) controller to attenuate the noisy effects of the external disturbance. A comparative analysis of results showed that a robust as well as an improved speed and torque dynamic performance was achieved with the state feedback (SFC) controller. A reduced periodic disturbance with percentage steady state error values of 24.17% and 23.51% was obtained with the SFC controller as compared to 38.0% and 38.37% obtained using a PID controller. The Eigen values obtained from the derived state feedback matrix (K) based on Ackerman's rule proved that the entire system operation is controllable and the performance index is marginally stable. All simulations were performed using MATLAB/SIMULINK version 2021.

Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements.

Based on the principles of neuromechanics, human arm movements result from the dynamic interaction between the nervous, muscular, and skeletal systems. To develop an effective neural feedback controller for neuro-rehabilitation training, it is important to consider both the effects of muscles and skeletons. In this study, we designed a neuromechanics-based neural feedback controller for arm reaching movements. To achieve this, we first constructed a musculoskeletal arm model based on the actual biomechanical structure of the human arm. Subsequently, a hybrid neural feedback controller was developed that mimics the multifunctional areas of the human arm. The performance of this controller was then validated through numerical simulation experiments. The simulation results demonstrated a bell-shaped movement trajectory, consistent with the natural motion of human arm movements. Furthermore, the experiment testing the tracking ability of the controller revealed real-time errors within one millimeter, with the tensile force generated by the controller's muscles being stable and maintained at a low value, thereby avoiding the issue of muscle strain that can occur due to excessive excitation during the neurorehabilitation process.

Establishment of a Mathematical Model for Parkinson's Disease Based on Complex Neural Networks and Design of Deep Brain Electrical Stimulation Treatment Plans.

This article introduces the modeling idea of complex neural networks based on the analysis of Parkinson's disease(PD) seizures. According to the Hodgkin-Huxley model of neurons and the synaptic connection theory between neurons, a theoretical model of the basal ganglia circuit was established. To reveal the working mechanism of the brain during the attack of nervous system disease. On this basis, the neural system model is combined with the control theory. Finally, an intermittent adaptive feedback controller is proposed to effectively suppress the onset of Parkinson's disease by stimulating the neuronal system accordingly.

Finite-time and fixed-time stabilization of multiple memristive neural networks with nonlinear coupling.

This brief presents the finite-time stabilization and fixed-time stabilization of multiple memristor-based neural networks (MMNNs) with nonlinear coupling. Under the retarded memristive theory, the generalized Lyapunov functional method, extended Filippov-framework and Laplacian matrix theory, we can realize both the finite-time stabilization and fixed-time stabilization problem of MMNNs by designing novel state-feedback controller and the corresponding adaptive controller with regulate parameters. Moreover, we assess the bounds of settling time for the both two kinds of stabilization respectively, and we deeply analyze the influence of initial desiring values and the linear growth condition of the controller on the system. Finally, the benefits of the proposed approach and the experimental analysis are demonstrated by numerical examples.

Adaptive output feedback controller design for high-order stochastic nonlinear systems with uncertain output function.

This paper concentrates on the adaptive output feedback controller design for a class of high-order stochastic nonlinear systems(SNSs) with uncertain output function. Firstly for the nominal system, a homogeneous observer and output feedback controller is put forward through adding a power integrator method. Secondly, a dynamic gain technique is adopted into the observer and controller to guarantee the convergence of original system states and boundedness of the dynamic gain in probability. Besides, the proposed controller design scheme can be also broadened to upper-triangular SNSs. Finally, two numerical examples are provided to indicate the effectiveness of the proposed method.

Robust output-feedback torque controller design for series elastic actuators and its application in multi-level control frameworks.

Robust output-feedback torque controller is developed for series elastic actuators (SEAs) in the presence of parameter uncertainties and external disturbances. The strong robustness of the proposed controller results from the filter-based observer which can estimate the velocity signals and the system lumped disturbance. The dynamic surface method is adopted to make the time-domain controller independent of any derivatives of the command reference, making the torque controller an ideal building block for multi-level control frameworks. The semiglobal stability of the closed-loop control system is proven under the assumption that only the state-independent uncertainty is bounded. The experimental results verify the effectiveness of the torque controller, and the implementation of two-level control frameworks, including the impedance control and SEA's load position control, further demonstrates its wide applicability.

Application of optimization algorithms to adaptive motion control for repetitive process.

The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for Optimization Algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation.

Developing and testing of an air dilution flow olfactometer with known rates of concentration change.

BACKGROUND: Concentration is a variable aspect of an odor signal and determines the operation range of olfactory receptor neurons (ORNs). A concentration increase is perceived as an odor stimulus. The role that the rate of concentration increase plays thereby has been studied with electrophysiological techniques in ORNs of the cockroach. A key prerequisite for these studies was the development of an air dilution flow olfactometer that allowed testing the same change in concentration at various rates. NEW METHOD: The rate of concentration change was controlled and varied by changing the mixing ratio of odor-saturated and clean air by means of proportional valves. Their input voltages were phase shifted by 180° to hold the mixed air at a particular constant volume flow rate. RESULTS: Using this stimulation technique, we identified, in a morphologically distinct sensillum on the cockroach's antenna, antagonistically responding ON and OFF ORNs which display a high sensitivity for slow changes in odor concentration. COMPARISON WITH EXISTING METHODS: The olfactometer is unique because it enables delivering slowly oscillating concentration changes. By varying the oscillation period, the individual effects of the instantaneous odor concentration and its rate of change on the ORNs' responses can be determined. CONCLUSIONS: The olfactometer provides a new experimental approach in the study of odor coding and opens the door for improved comparative studies on olfactory systems. It would be important to gain insight into the ORNs' ability to detect the rate of concentration change in other insects that use odors for orientation in different contexts.

Identification of the human postural control system through stochastic trajectory optimization.

BACKGROUND: System identification can be used to obtain a model of the human postural control system from experimental data in which subjects are mechanically perturbed while standing. However, unstable controllers were sometimes found, which obviously do not explain human balance and cannot be applied in control of humanoid robots. Eigenvalue constraints can be used to avoid unstable controllers. However, this method is hard to apply to highly nonlinear systems and large identification datasets. NEW METHOD: To address these issues, we perform the system identification with a stochastic system model where process noise is modeled. The parameter identification is performed by simultaneous trajectory optimizations on multiple episodes that have different instances of the process noise. RESULTS: The stochastic and deterministic identification methods were tested on three types of controllers, including both linear and nonlinear controller architectures. Stochastic identification tracked the experimental data nearly as well as the deterministic identification, while avoiding the unstable controllers that were found with a deterministic system model. COMPARISON WITH EXISTING METHOD: Comparing to eigenvalue constraints, stochastic identification has wider application potentials. Since linearization is not needed in the stochastic identification, it is applicable to highly nonlinear systems, and it can be applied on large data-sets. CONCLUSIONS: Stochastic identification can be used to avoid unstable controllers in human postural control identification.

Optimal robust model predictive reset control design for performance improvement of uncertain linear system.

This study aims to design a robust reset dynamic output feedback control (DOFC) for a class of uncertain linear systems. This procedure is performed as following. First, the elements of the robust DOFC are designed via the linear matrix inequality (LMI) technique such that closed-loop exponential stability is achieved. Second, reset law which contains value of after reset and a constraint for the reset action is determined. Genetic algorithm (GA) is applied to minimize the proposed objective function to find the reset times by using the specified after reset value for individual reset instances. To do this, a model-predictive-based optimization is adopted by using output information. The proposed robust controller is applied to two uncertain systems; distillation column, and B747-100/200 aircraft model. The merits of the proposed robust reset controller in improving transient performance are demonstrated by comparing its results with state-of-the-art methods.

Rehabilitation of the Parkinson's tremor by using robust adaptive sliding mode controller: a simulation study.

One of the efficient methods in controlling the Parkinson's tremor is Deep Brain Stimulation (DBS) therapy. The stimulation of Basal Ganglia (BG) by DBS brings no feedback though the existence of feedback reduces the additional stimulatory signal delivered to the brain. So this study offers a new adaptive architecture of a closed-loop control system in which two areas of BG are stimulated simultaneously to decrease the following three indicators: hand tremor, the level of a delivered stimulation signal in the disease condition, and the level of a delivered stimulation signal in health condition to the disease condition. One area (STN: subthalamic nucleus) is stimulated with an adaptive sliding mode controller and the other area (GPi: Globus Pallidus internal) with partial state feedback controller. The simulation results of stimulating two areas of BG showed satisfactory performance.

Robust generalized Mittag-Leffler synchronization of fractional order neural networks with discontinuous activation and impulses.

Fractional order system is playing an increasingly important role in terms of both theory and applications. In this paper we investigate the global existence of Filippov solutions and the robust generalized Mittag-Leffler synchronization of fractional order neural networks with discontinuous activation and impulses. By means of growth conditions, differential inclusions and generalized Gronwall inequality, a sufficient condition for the existence of Filippov solution is obtained. Then, sufficient criteria are given for the robust generalized Mittag-Leffler synchronization between discontinuous activation function of impulsive fractional order neural network systems with (or without) parameter uncertainties, via a delayed feedback controller and M-Matrix theory. Finally, four numerical simulations demonstrate the effectiveness of our main results.

H∞ control for a class of singular systems with state time-varying delay.

This paper is concerned with the H∞ control problem for a class of singular systems. The systems under consideration involve state time-varying delay. The aim of this paper is to design a dynamic feedback controller such that the resultant closed-loop system is regular, impulse-free and stable with given H∞ performance index. By using the Lyapunov-Krasovskii functional method, some delay-dependent sufficient criteria which guarantee the existence of the dynamic feedback controller and determine explicitly the parameters of the dynamic feedback controller are presented. Finally, some numerical examples are provided to show the effectiveness of the presented approaches.

Fuzzy dynamic output feedback H∞ control for continuous-time T-S fuzzy systems under imperfect premise matching.

This paper addresses a fuzzy dynamic output feedback H∞ control design problem for continuous-time nonlinear systems via T-S fuzzy model. The stability of the fuzzy closed-loop system which is formed by a T-S fuzzy model and a fuzzy dynamic output feedback H∞ controller connected in a closed loop is investigated with Lyapunov stability theory. The proposed fuzzy controller does not share the same membership functions and number of rules with T-S fuzzy systems, which can enhance design flexibility. A line-integral fuzzy Lyapunov function is utilized to derive the stability conditions in the form of linear matrix inequalities (LMIs). The boundary information of membership functions is considered in the stability analysis to reduce the conservativeness of the imperfect premise matching design technique. Two simulation examples are provided to demonstrate the effectiveness of the proposed approach.

The design of delay-dependent wide-area DOFC with prescribed degree of stability α for damping inter-area low-frequency oscillations in power system.

In this paper, the delay-dependent wide-area dynamic output feedback controller (DOFC) with prescribed degree of stability is proposed for interconnected power system to damp inter-area low-frequency oscillations. Here, the prescribed degree of stability α is used to maintain all the poles on the left of s=-α in the s-plane. Firstly, residue approach is adopted to select input-output control signals and the schur balanced truncation model reduction method is utilized to obtain the reduced power system model. Secondly, based on Lyapunov stability theory and transformation operation in complex plane, the sufficient condition of asymptotic stability for closed-loop power system with prescribed degree of stability α is derived. Then, a novel method based on linear matrix inequalities (LMIs) is presented to obtain the parameters of DOFC and calculate delay margin of the closed-loop system considering the prescribed degree of stability α. Finally, case studies are carried out on the two-area four-machine system, which is controlled by classical wide-area power system stabilizer (WAPSS) in reported reference and our proposed DOFC respectively. The effectiveness and advantages of the proposed method are verified by the simulation results under different operating conditions.

Actuator fault estimation and accommodation for switched systems with time delay: Discrete-time case.

This paper investigates the problems of actuator fault estimation and accommodation for discrete-time switched systems with state delay. By using reduced-order observer method and switched Lyapunov function technique, a fault estimation algorithm is achieved for the discrete-time switched system with actuator fault and state delay. Then based on the obtained online fault estimation information, a switched dynamic output feedback controller is employed to compensate for the effect of faults by stabilizing the closed-loop systems. Finally, an example is proposed to illustrate the obtained results.

Stability analysis of a class of fractional order nonlinear systems with order lying in (0, 2).

This paper investigates the stability of n-dimensional fractional order nonlinear systems with commensurate order 0 <α<2. By using the Mittag-Leffler function, Laplace transform and the Gronwall-Bellman lemma, one sufficient condition is attained for the local asymptotical stability of a class of fractional order nonlinear systems with order lying in (0, 2). According to this theory, stabilizing a class of fractional order nonlinear systems only need a linear state feedback controller. Simulation results demonstrate the effectiveness of the proposed theory.