Nonlinear adaptive robust control of tank bidirectional stabilizers with dead zone compensation based on extended state observer.

In this paper, the problem of highly performance motion control of tank bidirectional stabilizer with dead zone nonlinearity and uncertain nonlinearity is addressed. First, the electromechanical coupling dynamics model of bidirectional stabilizer is developed finely. Second, the dead zone nonlinearity in bidirectional stabilizer is characterized as the combination of an uncertain time-varying gain and a bounded disturbance term. Meanwhile, an adaptive robust controller with dead zone compensation is proposed by organically combining adaptive technique and extended state observer (ESO) through backstepping method. The adaptive technique is employed to reduce the impact of unknown system parameter and dead zone parameter. Furthermore, the ESO is constructed to compensate the lumped uncertainties including unmodeled dynamics and dead zone residual, and integrated together via a feedforward cancellation technique. Moreover, the adaptive robust control law is derived to ensure final global stability. In stability analysis, the asymptotic tracking performance of the proposed controller can be guaranteed as the uncertainty nonlinearities in tank bidirectional stabilizer are constant. It is also guaranteed to achieve bounded tracking performance when time-varying uncertainties exist. Extensive co-simulation and experimental results verify the superiority of the proposed strategy.

Finite-time compensation control with dead-zone estimation for a rehabilitative walker considering internal disturbance forces.

This study discusses a finite-time compensation tracking control method for a rehabilitative training walker. The dynamic model with input dead zone was constructed to describe the walker, and a finite-time disturbance forces observation method was proposed based on the impact mechanism on tracking performance. This approach is novel in that the disturbance forces were observed in reverse through their effects on tracking performance, thus successfully obtaining the disturbance forces of the walker. To ensure the practical finite-time stability of the system, the nonlinear finite-time compensation tracking controller with stochastic configuration networks (SCN) dead-zone estimation was built for the rehabilitative walker. Simulation results and comparative analyses confirmed that the proposed compensation control method effectively restrains dead zone and internal disturbance forces.

Enhancing microalgal biomass production in lab-scale raceway ponds through innovative computational fluid dynamics-based electrode deflectors.

The design of novel electrode deflector structures (EDSs) introduced a promising strategy for enhancing raceway ponds performance, increasing carbon fixation, and improving microalgal biomass accumulation. The computational fluid dynamics, based flow field principles, proved that the potency of arc-shaped electrode deflector structures (A-EDS) and spiral electrode deflector structures (S-EDS) were optimal. These configurations yielded superior culture effects, notably reducing dead zones by 9.1% and 11.7%, while elevating biomass increments of 14.7% and 11.5% compared to the control, respectively. In comparison to scenarios without electrostatic field application, the A-EDS group demonstrated pronounced post-stimulation growth, exhibiting an additional biomass increase of 11.2%, coupled with a remarkable 23.6% surge in CO2 fixation rate and mixing time reduction by 14.7%. A-EDS and S-EDS, combined with strategic electric field integration, provided a theoretical basis for promoting microalgal biomass production and enhancing carbon fixation in a raceway pond environment to similar production practices.

Sliding-mode fault-tolerant control of the six-rotor UAV with dead-zone-input under event-triggered mechanism.

This paper investigates the problem of event-triggered mechanism(ETM)-based sliding-mode fault-tolerant control (FTC) for a six-rotor Unmanned Aerial Vehicle (UAV) with dead zone input (DZI) cases, considering potential actuator and sensor faults. Initially, a dynamic ETM is designed, followed by the development of a non-fragile observer utilizing this designed ETM. An integral sliding surface (SS) is then designed in the observation space, and the system is augmented and treated as a variable time delay system. Subsequently, sufficient conditions to ensure the stability of the augmented system with an H∞ performance index γ are obtained using the Lyapunov-Krasovskii function. Next, a sliding mode control (SMC) law is formulated to guide the sliding variables to the SS in finite time. Furthermore, sufficient conditions for ensuring system stability with an H∞ performance index γ are decoupled, and the calculation methods for the non-fragile observer gain matrix and the sliding mode gain matrix are obtained. Finally, to validate the effectiveness of the proposed method in this paper, simulation experiments are conducted.

Active fault-tolerant control for the dual-valve hydraulic system with unknown dead-zone.

This paper proposes a method for high-performance motion control of the dual-valve hydraulic system subject to parameter and model uncertainties, unknown proportional valve dead-zone, and servo valve fault. By constructing a detailed dual-valve fault system model (DFSM), a disturbance observer-based adaptive robust fault-tolerant controller is proposed via the backstepping method. This controller integrates a model-based fault detection algorithm for real-time fault monitoring and subsequent controller reconfiguration. Additionally, the DFSM-based adaptive robust control (ARC) technique is applied to handle the unknown dead-zone problem and other nonlinearities, ensuring precise control. Once the servo valve fault occurs, a nonlinear observer estimates the fault and collaborates with the ARC to establish a reconfigured controller, thereby maintaining motion control. The effectiveness of the proposed method has been experimentally verified.

Dead-zone-compensated design as general method of flow field optimization for redox flow batteries.

One essential element of redox flow batteries (RFBs) is the flow field. Certain dead zones that cause local overpotentials and side effects are present in all conventional designs. To lessen the detrimental effects, a dead-zone-compensated design of flow field optimization is proposed. The proposed architecture allows for the detection of dead zones and their compensation on existing flow fields. Higher reactant concentrations and uniformity factors can be revealed in the 3D multiphysical simulation. The experiments also demonstrate that at an energy efficiency (EE) of 80%, the maximum current density of the novel flow field is 205 mA cm-2, which is much higher than the values for the previous ones (165 mA cm-2) and typical serpentine flow field (153 mA cm-2). Extensions of the design have successfully increased system EE (2.7 to 4.3%) for a variety of flow patterns. As a result, the proposed design is demonstrated to be a general method to support the functionality and application of RFBs.

An Optically Pumped Magnetometer with Omnidirectional Magnetic Field Sensitivity.

In mobile applications such as geomagnetic surveying, two major effects hamper the use of optically pumped magnetometers: dead zones, sensor orientations where the sensors signal amplitude drops; and heading errors, a dependence of the measured magnetic field value on the sensor orientation. We present a concept for an omnidirectional magnetometer to overcome both of these effects. The sensor uses two cesium vapor cells, interrogated by circularly-polarized amplitude-modulated laser light split into two beams propagating perpendicular to each other. This configuration is experimentally investigated using a setup wherein the laser beam and magnetic field direction can be freely adjusted relative to each other within a magnetically shielded environment. We demonstrate that a dead-zone-free magnetometer can be realized with nearly isotropic magnetic-field sensitivity. While in the current configuration we observe heading errors emerging from light shifts and shifts due to the nonlinear Zeeman effect, we introduce a straightforward approach to suppress these systematic effects in an advanced sensor realization.

Microplastic transport dynamics in surcharging and overflowing manholes.

The transport of microplastics within urban water systems remains poorly understood, with little prior research on their behaviour within manhole configurations. This study represents the first to measure and model the transport dynamics of microplastics within circular and square manholes under different hydraulic scenarios. The transport and fate of polyethylene (PE) was quantified and compared to solutes (Rhodamine WT dye) using energy losses, residence time distributions (RTDs), and mixing models within surcharging and overflowing manholes. The bulk mass of solute and PE concentrations followed similar flow paths across all conditions except for 17.3 ± 7.9 % of PE mass that was immobilized in a dead zone above the inlet pipe for manholes with a surcharge to pipe diameter ratio ≥2. Consequently, these microplastics only exit after a significant change in hydraulic regime occurs, causing microplastics to be at risk of being contaminated over a prolonged duration. No significant mixing differences for PE and solutes were found between manhole geometries. The deconvolution method outperformed the ADZ model with goodness of fit (Rt2) values of 0.99 (0.60) and 1.00 (0.89) for PE and solute mixing, respectively. This establishes the deconvolution method as the most accurate and appropriate model to accurately predict microplastic mixing in manholes and urban drainage systems.

Electrocardiogram signal compression using adaptive tunable-Q wavelet transform and modified dead-zone quantizer.

The electrocardiogram (ECG) signals are commonly used to identify heart complications. These recordings generate large data that needed to be stored or transferred in telemedicine applications, which require more storage space and bandwidth. Therefore, a strong motivation is present to develop efficient compression algorithms for ECG signals. In the above context, this work proposes a novel compression algorithm using adaptive tunable-Q wavelet transform (TQWT) and modified dead-zone quantizer (DZQ). The parameters of TQWT and threshold values of DZQ are selected using the proposed Sparse-grey wolf optimization (Sparse-GWO) algorithm. The Sparse-GWO is proposed in this work to reduce the computation time of the original GWO. Moreover, it is also compared with some popular algorithms such as original GWO, particle swarm optimization (PSO), Hybrid PSOGWO, and Sparse-PSO. The DZQ has been utilized to perform thresholding and quantization. Then, run-length encoding (RLE) has been used to encode the quantized coefficients. The proposed work has been performed on the MIT-BIH arrhythmia database. Quality assessment performed on reconstructed signals ensure the minimal impact of compression on the morphology of reconstructed ECG signals. The compression performance of proposed algorithm is measured in terms of the following evaluation matrices: percent root-mean-square difference (PRD1), compression ratio (CR), signal-to-noise ratio (SNR), and quality score (QS1). The obtained average values are 3.21%, 20.56, 30.62 dB, and 7.79, respectively.

Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach.

This paper focuses on the adaptive reinforcement learning-based optimal control problem for standard nonstrict-feedback nonlinear systems with the actuator fault and an unknown dead zone. To simultaneously reduce the computational complexity and eliminate the local optimal problem, a novel neural network weight updated algorithm is presented to replace the classic gradient descent method. By utilizing the backstepping technique, the actor critic-based reinforcement learning control strategy is developed for high-order nonlinear nonstrict-feedback systems. In addition, two auxiliary parameters are presented to deal with the input dead zone and actuator fault respectively. All signals in the system are proven to be semi-globally uniformly ultimately bounded by Lyapunov theory analysis. At the end of the paper, some simulation results are shown to illustrate the remarkable effect of the proposed approach.

Observer-based adaptive fuzzy output feedback control for functional constraint systems with dead-zone input.

This paper develops an adaptive output feedback control for a class of functional constraint systems with unmeasurable states and unknown dead zone input. The constraint is a series of functions closely linked to state variables and time, which is not achieved in current research results and is more general in practical systems. Furthermore, a fuzzy approximator based adaptive backstepping algorithm is designed and an adaptive state observer with time-varying functional constraints (TFC) is constructed to estimate the unmeasurable states of the control system. Relying on the relevant knowledge of dead zone slopes, the issue of non-smooth dead-zone input is successfully solved. The time-varying integral barrier Lyapunov functions (iBLFs) are employed to guarantee that the states of the system remain within the constraint interval. By Lyapunov stability theory, the adopted control approach can ensure the stability of the system. Finally, the feasibility of the considered method is conformed via a simulation experiment.

Global stability of a continuous bioreactor model under persistent variation of the dilution rate.

In this work, the global stability of a continuous bioreactor model is studied, with the concentrations of biomass and substrate as state variables, a general non-monotonic function of substrate concentration for the specific growth rate, and constant inlet substrate concentration. Also, the dilution rate is time varying but bounded, thus leading to state convergence to a compact set instead of an equilibrium point. Based on the Lyapunov function theory with dead-zone modification, the convergence of the substrate and biomass concentrations is studied. The main contributions with respect to closely related studies are: i) The convergence regions of the substrate and biomass concentrations are determined as function of the variation region of the dilution rate (D) and the global convergence to these compact sets is proved, considering monotonic and non-monotonic growth functions separately; ii) several improvements are proposed in the stability analysis, including the definition of a new dead zone Lyapunov function and the properties of its gradient. These improvements allow proving convergence of substrate and biomass concentrations to their compact sets, while tackling the interwoven and nonlinear nature of the dynamics of biomass and substrate concentrations, the non-monotonic nature of the specific growth rate, and the time-varying nature of the dilution rate. The proposed modifications are a basis for further global stability analysis of bioreactor models exhibiting convergence to a compact set instead of an equilibrium point. Finally, the theoretical results are illustrated through numerical simulation, showing the convergence of the states under varying dilution rate.

NN event-triggered finite-time consensus control for uncertain nonlinear Multi-Agent Systems with dead-zone input and actuator failures.

This paper develops a Neural Network (NN) event-triggered finite-time consensus control method for uncertain nonlinear Multi-Agent Systems (MASs) with dead-zone input and actuator failures. In practical applications, actuator failures would inevitably arise in MASs. And the time, pattern, and value of the failures are unknown. Besides, the actuators of MASs also suffer from dead-zone nonlinearity. No matter actuator failures or dead-zone input would dramatically affect the performance and stability of MASs. To address these issues, finite-time adaptive controllers capable of simultaneously compensating for actuator failures and dead-zone input are constructed by adopting the backstepping technology. Meanwhile, the NN control scheme is adopted to handle the unknown nonlinear dynamics of each agent. Furthermore, an event-triggered control mechanism is established that no longer requires continuous communication on the control network. Under the proposed control method, all followers achieve finite-time synchronization, irrespective of the presence of limited bandwidth, unknown failures, and dead-zone input. These results are demonstrated by simulations.

The Negative-Charge-Triggered "Dead Zone" between Electrode and Current Collector Realizes Ultralong Cycle Life of Aluminum-Ion Batteries.

Typically, volume expansion of the electrodes after intercalation of active ions is highly undesirable yet inetvitable, and it can significantly reduce the adhesion force between the electrodes and current collectors. Especially in aluminum-ion batteries (AIBs), the intercalation of large-sized AlCl4 - can greatly weaken this adhesion force and result in the detachment of the electrodes from the current collectors, which seems an inherent and irreconcilable problem. Here, an interesting concept, the "dead zone", is presented to overcome the above challenge. By incorporating a large number of OH- and COOH- groups onto the surface of MXene film, a rich negative-charge region is formed on its surface. When used as the current collector for AIBs, it shields a tiny area of the positive electrode (adjacent to the current collector side) from AlCl4 - intercalation due to the repulsion force, and a tiny inert layer (dead zone) at the interface of the positive electrode is formed, preventing the electrode from falling off the current collector. This helps to effectively increase the battery's cycle life to as high as 50 000 times. It is believed that the proposed concept can be an important reference for future development of current collectors in rocking chair batteries.

An extended 3-compartment model for describing step change experiments in pharmaceutical twin-screw feeders at different refill regimes.

Residence time distributions (RTDs) are a valuable tool for product tracking in the unit operations of a continuous line for manufacturing pharmaceutical oral solid dosage (OSD) and the integrated system itself. The first unit operation in such a continuous line in which extended intermixing can occur, is typically a feeder. The RTD of a feeder can be obtained by performing tracer experiments with a tracer material. A physical interpretation can be given to the observed tracer concentration responses by fitting a tanks-in-series (TIS) or compartmental model to it. Consequently, the internal mixing behaviour inside the feeder hopper can be rationalized. However, typically, a constant volume is assumed for the tanks or compartments in these models. This has led to several publications where the experimental set-up does not violate the constant volume assumption, i.e. one performs refills at a high hopper fill level. Here, we step away from this assumption and develop a set of differential equations for a 3-compartment model in order to account for a non-constant volume of the compartments. Moreover, the model distinguishes between a bypass trajectory formed by the agitator inside the feeder and an inner mixing volume, in which the tracer concentration lags on the tracer concentration in the bypass volume. This compartmentalization was inspired by the results obtained in a previous study using a spatial sampling method to assess the tracer concentration throughout the feeder hopper for different experimental runtimes. The developed model successfully describes the step responses for different refill regimes: the standard smooth first order plus dead time response (FOPDT) for a high refill regime and the more complex step response, including dips in the rising phase of the curve, for the low refill regime. As a consequence, a more thorough understanding of the complex mixing behaviour inside the feeder is obtained, which allows for an improved traceability. Next to that, the model delivers enhanced knowledge on the interaction between the residence time and the refill regime. The developed model was fitted to a data set, containing step change experiments for different pharmaceutical materials (Tablettose 80 (T80), Microcelac 100 (MCL), and Avicel PH101 (MCC)), different mass flow rates, and refill regimes. The experimentally observed phenomena could be reliably described by the proposed model. The model showed an improved transferability compared to typical TIS models.

Extending Non-Ambiguity Range of Dual-Comb Ranging for a Mobile Target Based on FPGA.

Dual-comb ranging (DCR) is an important method in absolute distance ranging because of its high precision, fast acquisition rate, and large measuring range. DCR needs to obtain precise results during distance measurements for a mobile target. However, the non-ambiguity range (NAR) is a challenge when pushing the dual-comb ranging to the industry field. This paper presents a solution for extending NAR by designing an algorithm and realizing it on a field-programmable gate array (FPGA). The algorithm is robust when facing the timing jitter in the optical frequency comb. Without averaging, the Allan deviation of the results in 1 ms is ∼3.89 µm and the Allan deviation of the results is ∼0.37 µm at an averaging time of 100 ms when the target object is standstill near the NAR. In addition, several ranging experiments were conducted on a mobile target whose speed was from ∼5 mm/s to ∼10 mm/s. The experimental results verify the effectiveness and robustness of our design. The implemented design is an online and real-time data processing unit that shows great industrial potential for using the DCR system.

Observer-based adaptive neural tracking control for a class of nonlinear systems with prescribed performance and input dead-zone constraints.

This paper investigates the problem of output feedback neural network (NN) learning tracking control for nonlinear strict feedback systems subject to prescribed performance and input dead-zone constraints. First, an NN is utilized to approximate the unknown nonlinear functions, then a state observer is developed to estimate the unmeasurable states. Second, based on the command filter method, an output feedback NN learning backstepping control algorithm is established. Third, a prescribed performance function is employed to ensure the transient performance of the closed-loop systems and forces the tracking error to fall within the prescribed performance boundary. It is rigorously proved mathematically that all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded and the tracking error can converge to an arbitrarily small neighborhood of the origin. Finally, a numerical example and an application example of the electromechanical system are given to show effectiveness of the acquired control algorithm.

Behavior of the saccadic system: Metrics of timing and accuracy.

The behavior of saccades in response to a peripheral target is discussed. The saccade latency comprises sensory and motor processing delays of about 80ms, leaving on average more than 100ms for central processing. Many factors influence the latter. Yet, programming express saccades requires little to no central processing time. Typical saccades are hypometric by about 10%, which seems to be a deliberate strategy. A correction saccade requires only about 50ms of central processing. There is no strict dead zone for saccades, as they can be elicited by target jumps as small as 0.05deg. There seems to be no strict refractoriness in the system either, because saccade metrics can be continuously modified during the preparation interval by new target information. This suggests semi-independent processes for the "when" and "where" of saccades, which is incorporated into a neurophysiologically-inspired model. Saccades are not kept in retinotopic coordinates but are goal-directed by incorporating intervening changes in eye position. Although the updating mechanism is unclear, there is strong evidence that it involves the use of efference copy information (the outflow theory). Although the spatial percept of a target may be erroneous around saccades, the motor system seems to be more accurate. The chapter closes with a discussion on the potential function of microsaccades and slow drifts, when fixating a target.

Challenges in tracing material flow passing a loss-in-weight feeder in continuous manufacturing processes.

Loss-in-weight feeders are an integral part of most continuous manufacturing processes, ensuring a constant mass flow. The feeders cause a significant degree of back-mixing in such lines. Understanding back-mixing is essential for the treatment of disturbances. However, feeders refilled semi-continuously contradict the common theory assuming steady-state. This study aims at understanding dynamic back-mixing and related phenomena. Low filling levels of a feeder are investigated using a fluorescent tracer. These investigations prove an impact of the addition of material probably caused by a non-uniform draw-in of the screws and dead material in the hopper. In turn, the dead material accounts for up to 50 % of the material in the hopper. Possible evidence of dead zones at higher filling levels and in feeders from literature are discussed additionally. Steady-state models from literature are extended to represent the observations and back-mixing at all filling levels. This extension reduces the root-mean-squared deviation of the model from the experimental data by 41%. The model predicts different responses to similar disturbances depending on the filling. This state-dependent back-mixing and the observed dead zones are challenging for diverting non-conforming material and material traceability. Therefore, these phenomena should be considered in selecting and operating feeders.

Autoencoder-based detection of near-surface defects in ultrasonic testing.

Defect detection during pulse-echo ultrasonic testing (UT) is challenging when defects are located in a dead zone where the echoes from the defects are overshadowed by disturbances from the initial ringing signal of the UT transducer. The time-gate method is one of the most widely used approaches in UT to filter out such unwanted components, but defects in the dead zone are virtually impossible to detect using conventional methods. This paper proposes an autoencoder-based end-to-end ultrasonic testing method to detect defects within the dead zone of a transducer. The autoencoder is designed to predict the normal behavior of ultrasonic signals including disturbances, thus enabling the identification of even subtle deviations made by defects. To advance the performance of the autoencoder further with a limited amount of training data, a two-step training procedure is presented, involving training using pure normal signals measured from a defect-free specimen and re-training using pseudo-normal samples identified by the autoencoder with a smart thresholding strategy. This two-step procedure enables us to develop an adaptive autoencoder model that can be effectively employed to process the newly measured ultrasonic signals. For a demonstration of the proposed method, UT-based B-scan inspections of aluminum blocks with near-surface defects are conducted. The results suggest that the proposed method outperforms the conventional gate-based inspection approach with regard to its ability to identify the sizes and locations of near-surface defects.