Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

Analysis and proposition of limiting current density measurement protocol

Limiting current density at different temperatures, backpressures, and balance gases can be used to separate molecular diffusion resistance, Knudsen diffusion resistance and local transport resistance of membrane electrode assembly (MEA). However, the measurement of limiting current density has no unified protocol. The diverse choices in the literature, either in the control of current or voltage or in the atmosphere like relative humidity and O₂ concentrations, make it difficult to compare the results and identify the true bottleneck hindering the mass transport. In this work, the current-voltage curves obtained by current scanning/stepping and voltage scanning/stepping methods under dilute O₂ of different concentrations and a wide range of relative humidity were measured and analyzed systematically. It is found that the voltage stepping method is superior to the other three ways of control for the reliable determination of the limiting current density. Aided with simultaneous electrochemical impedance spectroscopy measurement, the limiting current density can be determined with pinpoint accuracy. When the limiting current density is just used to qualitatively evaluate different MEA, the voltage scanning method can be used instead for its high time efficiency. The selection of the atmosphere also plays an important role in suppressing the distortion from excessive water and reducing the spurious contribution from proton conduction resistance. It is found that O₂ concentrations at 0.5 vol% and relative humidity at 90% can give the best estimation of O₂ transport resistance in membrane electrode assembly.     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.     

Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

Linear correlation between pH value of stimulated beef and electrical current intensity

The aim of the present study was to determine mathematical relationships between pH changes in beef 24 h post-slaughter and changes in the intensity of electrical current flowing through bull and heifer carcasses during high-voltage electrical stimulation. The electrical stimulation was applied 40 min postmortem for 120 s. The pH values of m. longissimus thoracis et lumborum were analyzed in the function of electrical current intensity changes and its change during electrical stimulation. Mathematical linear correlations of the y = ax ± b type were demonstrated between pH values at 2, 6, and 24 h postmortem and the initial (I_i) and ultimate (I_u) electrical current intensity values, the difference between them and the initial pH values determined before electrical stimulation. High multiple correlation coefficients (R² = 0.416, α ≤ 0.001) between I_u and pH values 24 h post-slaughter enabled concluding that there is a possibility to predict a pH value of stimulated carcass with high accuracy, and thus also beef quality, based merely on the ultimate electrical current intensity values.     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.     

Feedforward control of the transverse strip profile in hot-dip galvanizing lines

Hot-dip galvanizing is a standard technology to produce coated steel strips. The primary objective of the galvanizing process is to establish a homogeneous zinc layer with a defined thickness. One condition to achieve this objective is a uniform transverse distance between the strip and the gas wiping dies, which blow off excessive liquid zinc. Therefore, a flat strip profile at the gas wiping dies is required. However, strips processed in such plants often exhibit residual curvatures which entail unknown flatness defects of the strip. Such flatness defects cause non-uniform air gaps and hence an inhomogeneous zinc coating thickness. Modern hot-dip galvanizing lines often use electromagnets to control the transverse strip profile near the gas wiping dies. Typically, the control algorithms ensure a flat strip profile at the electromagnets because the sensors for the transverse strip displacement are also located at this position and it is unfeasible to mount displacement sensors directly at the gas wiping dies. This brings along that in general a flatness defect remains at the gas wiping dies, which in turn entails a suboptimal coating.In this paper, a model-based method for a feedforward control of the strip profile at the position of the gas wiping dies is developed. This method is based on a plate model of the axially moving strip that takes into account the flatness defects in the strip. First, an estimator of the flatness defects is developed and validated for various test strips and settings of the plant. Using the validated mathematical model, a simulation study is performed to compare the state-of-the-art control approach (flat strip profile at the electromagnets) with the optimization-based feedforward controller (flat strip profile at the gas wiping dies) proposed in this paper. Moreover, the influence of the distance between the gas wiping dies and the electromagnets is investigated in detail.     

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.     

A hybrid homogeneously weighted moving average control chart for process monitoring

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ₁ and λ₂ . The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data.