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In this paper, the adaptive control problem is investigated for a class of interconnected nonlinear systems (INS) with unknown interconnection delays and input saturations. For state unmeasurable problem, a full dimension state observer (SO) is designed to estimate the inaccessible state variables. Broad learning system (BLS), a novel nonlinear approximation technique, is introduced in this study to identify the unknown dynamics and could achieve better approximation performance. An adaptive distributed control (DC) scheme is proposed for the uncertain time-delay INS without input saturation, which guarantees that the stability of the closed loop INS. On this basis, the input saturation problem is further considered, an online approximation smooth function is added into the distributed adaptive tracking controller, such that the closed-loop INS have the anti-input saturation capability. In terms of Lyapunov theory, all the signals in the closed-loop uncertain time-delay INS with input saturation are proved to be uniformly ultimately bounded (UUB) and the tracking errors could converge to a small neighbourhood of zero. Finally, one simulation example performed on two parallel inverted pendulum cars demonstrates the superiority of the developed distributed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed fault‐tolerant tracking control (FTTC) problem for multiple unmanned aerial vehicles (UAVs) in the presence of state constraints, input quantization and actuator failures. The unified barrier function (UBF) is first developed to convert the original constrained system model into an unconstrained one, which accomplishes the dynamic constraints of all states. Moreover, a cerebellar model neural network (CMNN) is introduced to estimate the lumped effect of unknown disturbances and actuator failures. Then, a distributed formation flight fault‐tolerant controller (FTC) is designed to guide the UAVs along the desired trajectories based on sliding mode manifold and dynamic surface control technique. Meanwhile, the unknown input chattering and hysteresis quantizer errors in the UAVs system are simultaneously handled by adding the compensation term of FTC. Furthermore, the finite time stability analysis is implemented for all tracking errors and synchronization errors of the cooperative flight control system (CFCS). Finally, a comparative simulation is conducted to demonstrate the effectiveness of the FTTC scheme proposed in this study. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the fault is hard to identify during the operation of the rolling bearing, a fault diagnosis method of maximum correlation kurtosis deconvolution and improved least squares support vector machine is proposed. First of all, the method uses the maximum correlation kurtosis deconvolution to extract bearing vibration signals under different operating conditions. Then the least square support vector machine is used to supervise the extracted vibration signals. At the same time, improved cuckoo search algorithm is used to solve the problem that the kernel parameters and penalty factors of LSSVM fall into the local optimum and the convergence accuracy is poor in the optimization process, and improve the recognition rate of fault diagnosis. Bearing data were measured in different running states to verify the effectiveness of the method. The experimental results show that improved algorithm can effectively identify all types of rolling bearing status with high accuracy. It was a reliable method of bearing fault diagnosis.

In plastic injection molding (PIM), the process parameters determine the quality and productivity of molded parts. The traditional injection molding process analysis method mainly relies on production experience. It is lack of advanced and rationality and seriously increases production costs. In this paper, a hybrid multiobjective optimization method is proposed to minimize the warpage, volumetric shrinkage and cycle time. The method integrates orthogonal experimental design, numerical simulation, and the metamodel method with multiobjective optimization. The orthogonal experiment chooses seven parameters as the design variables to generate sampling data and determines key factors that affect product quality by the numerical simulation. A gradient-enhanced Kriging (GEK) surrogate model strategy is introduced to construct the response predictors to calculate objective responses in the global design space. Multipopulation differential evolution (MPDE) is conducted to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. This study shows that the proposed GEK-MPDE method can reduce warpage, volumetric shrinkage and cycle time by 5.7 %, 4.7 %, and 18.1 %, respectively. It helps plastic industry to realize collaborative scheduling of multiple tasks between different production lines by providing a low-cost and effective dynamic control method. [ABSTRACT FROM AUTHOR]

In this article, the distributed tracking control problem is studied for the formation flight systems of unmanned aerial vehicles (UAVs) in presence of input constraints and actuator faults. Different from the traditional approximation technique, such as radial basis function neural networks and fuzzy logic systems, the novel broad learning system approximation technique is introduced in this study to identify the unknown lumped disturbances including external disturbances, input constraints and additive actuator faults. Combining dynamic surface control technique with the fractional order sliding mode control, a distributed fault tolerant tracking control strategy is developed for the formation flight systems of UAVs. Besides, the adverse effect of multiplicative actuator faults is compensated by the Nussbaum function, such that the satisfactory cooperative tracking control performance of the formation flight systems is obtained. Finally, a simulation experiment is given to demonstrate the superiority of the proposed control scheme compared with other existing results. [ABSTRACT FROM AUTHOR]

The concentration analyzer with high precision and wide range is the core device for monitoring the fermentation process. In this work, we designed and proposed a lowcost three-electrode glucose analyzer based on a self-made screen-printed enzyme biosensor chip, which has a Prussian blue (PB) nanocubic structure and leads to high sensitivity of 117.31 μAmM-1cm-2. The hardware design of the glucose analyzer can be divided into five critical parts, including digital, signal treatment system, power supply, motor-driven and the host computer. The signal treatment system is used to collect, convert and amplify the weak current signal generated by the biosensor. The digital circuit of the central processing unit of the analyzer is designed using the STM32F407ZET6. Besides, an external analog-to-digital converter is used to achieve high precision A/D conversion. The stability of the potentiostat is ensured by designing the precision power supply, hardware filtering, and algorithm filtering. The experimental results show that the glucose analyzer has a wide linear detection range from 1g/L to 120g/L and the coefficient of variation at 1g/L is 0.038, which exhibits excellent performance in stability and detection accuracy. The analyzer can be applied in the future for in-situ measurement of glucose concentration for its wide-range and high-precision detection capabilities.

In this paper, a closed-loop brain stimulation control problem is investigated using the nonsingular integral terminal sliding mode (NITSM) control approach. First, the thalamocortical model of epilepsy seizure is given, which is composed of the cortical PY-IN subnetwork and the subcortical RE-TC subsystem. Then, a nonsingular integral terminal sliding mode surface is designed utilizing the derived output tracking error, and the stability of the sliding mode dynamics is proved by Lyapunov approach. Furthermore, a disturbance observer (DOB) based NITSM controller design approach is proposed for the established thalamocortical model, and the reachability of the closed-loop control system under the designed controller is analyzed using Lyapunov theory. Finally, simulation results are given to illustrate the effectiveness and superiority of the designed control scheme. [ABSTRACT FROM AUTHOR]

In this study, a prescribed performance adaptive fault tolerant tracking control scheme is presented for a class of nonlinear large‐scale systems with time delay interconnection, dead zone input, and actuator fault. The radial basis function neural networks are used to approximate unknown nonlinear functions. Different from the barrier Lyapunov functions used to achieve the symmetrical prescribed performance, a new error transformation is introduced in this study to achieve the desired asymmetrical prescribed performance. In addition, Nussbaum function is introduced to solve the difficulties caused by dead zone input and actuator fault. Based on the appropriate Lyapunov–Krasovskii functions, the effect of time delay interconnection could be compensated. By using backstepping procedures, an adaptive fault tolerant tracking control approach is developed for the considered large‐scale systems, and the stability of the closed‐loop systems is analyzed by Lyapunov theory. Meanwhile, the prescribed performance of the tracking error could be guaranteed. Finally, the effectiveness of the proposed control approach is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]