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In this paper, a finite time fault estimation and fault‐tolerant control method is proposed for a class of uncertain nonlinear systems that have both actuator faults and sensor faults. First, the outputs of the original system pass through the low‐pass filters to obtain the augmented states, and then an augmented system is composed of the states of the low‐pass filters and the original system, so as to realize the conversion of the sensor faults of the original system into partial actuator faults of the augmented system. Next, design state observers to reconstruct the unknown states of the augmented system, and develop extended state observers to estimate the actuator faults and disturbance of the augmented system. Again, the fault‐tolerant controllers are completed based on the backstepping control and the finite time control, and the finite time convergences of the designed observers and controllers are proved by theoretical analyses. Finally, based on a permanent magnet synchronous motor (PMSM) speed system, the simulation research and dSPACE simulated experiment research are conducted to verify the effectiveness of the proposed control method and its feasibility in the actual application. [ABSTRACT FROM AUTHOR]

Summary: For the complex time‐varying uncertain nonlinear speed and tension system of reversible cold strip rolling mill, an adaptive sliding mode observers (ASMOs)‐based dissipative Hamilton finite time optimization control method is given in this article. First, the ASMOs are developed to observe rolling mill system's unmatched uncertainties, and their observation errors can converge to the origin in finite time. Second, combined with rolling mill system's physical structure characteristics, its dissipative Hamilton model is established by pre‐feedback processing, and then the dissipative Hamilton finite time controllers are designed based on interconnection and damping configuration and energy shaping method, and the system states can converge in finite time. Third, the adaptive mutation particle swarm optimization algorithm is adopted to optimize the controller parameters, which can further improve rolling mill system's control performance. Finally, the simulation results verify the validity of the given method based on actual field data. [ABSTRACT FROM AUTHOR]

In this paper, fault estimation and active fault‐tolerant control are studied for a class of nonlinear systems with simultaneous actuator and sensor faults, as well as unknown external disturbances. Firstly, the state equation of a class of nonlinear systems is transformed into an augmented system state equation by extending the sensor fault as an auxiliary state. Then, a novel fault estimation observer based on iterative learning with unknown inputs is designed to estimate the system state, as well as actuator and sensor faults. Subsequently, by using the fault estimation information, a dynamic output feedback active fault‐tolerant control scheme is proposed to compensate for the influence of faults on the system. Lyapunov stability theory is used to prove the stability of the closed‐loop system and the convergence of the fault estimation observer. The gain matrices of the fault estimation observer and fault‐tolerant controller are obtained by solving linear matrix inequalities. Furthermore, the paper avoids the use of the λ norm in the convergence proof of the conventional iterative learning algorithm, which reduces the amount of calculation in the derivation process. Finally, the effectiveness and accuracy of the proposed method are verified through simulation of the DC motor angular velocity system. [ABSTRACT FROM AUTHOR]

In order to solve the tracking problem of oscillation displacement system of continuous casting mold driven by a servo motor in the present of the time‐varying load, parameter uncertainties, unknown dynamics and external disturbances, a robust iterative learning control (ILC) via adaptive backstepping is proposed in this paper. Firstly, backstepping technique is used to design controller due to the mismatched disturbances. Secondly, the periodic component of the lumped disturbances is estimated by ILC method, while the aperiodic component is attenuated by switching term with adaptive gain. In addition, a time‐varying boundary layer approach is employed to overcome the identical initial condition limitation of the ILC. Through a proper composite energy function, the convergence of the closed‐loop system is analyzed. Finally, simulation and experimental results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: For the speed and tension system of the reversible cold strip rolling mill with output constraints, parameter perturbations and load disturbance, a fixed‐time prescribed performance optimization control method is proposed based on disturbance observers in this article. First, the disturbance observers are constructed to estimate the system's unmatched uncertainties, and the observer errors can converge in fixed time. Second, a time‐varying logarithmic barrier Lyapunov function (TLBLF) is given and combined with the command filtered backstepping approach, the fixed‐time control and the prescribed performance control to complete the controller designs for the speed and tension system of the cold strip rolling mill, which make the system states converge in fixed time and are always constrained within predefined ranges. Third, particle swarm optimization‐gray wolf optimization (PSO_GWO) hybrid intelligent algorithm is used to optimize the main control parameters of the designed controllers, which further improves the convergence speed and steady‐state accuracy of the rolling mill system. Theoretical analysis proves that the proposed control method can ensure the closed‐loop system is stable in fixed time. Finally, the simulation comparison study is carried out by using the field actual data of a reversible cold strip mill system, and the simulation results verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This survey reports for the first time the changed of quality of fermented cocoa (Theobroma cacao L.) beans. The quality evaluation and simultaneous detection of amino acids, flavor, procyanidin, color, fat, protein, antioxidant activity, and enthalpy were obtained for different fermentation stages of cocoa beans. The results showed that total essential amino acids contents ranged from 2.64 g/100 g to 3.68 g/100 g. A total of 88 compounds identified at the end of the fermentation belonged to alcohols, acids, esters, ketones, pyrazines, aldehydes, and terpenoids. One of the chemical groups that were present in highest abundance in the consummation treatments was acids, representing 56.04% of the total extracted area, followed by alcohols (22.95%) and ketones (9.40%). The colors of the beans in different fermentation stages were different, from deep purple to deep red‐brown. Fermented cocoa beans were shown to be 53.45% and 13.51% bean butter and protein content, respectively. The value of denaturation enthalpy (ΔH) ranged from 30.4 (J/g) to 43.38 (J/g). The 3‐day fermented sample had the highest ΔH (43.38 J/g). When the fermentation process was complete, the procyanidin concentration of the beans decreased, with the final yield of procyanidin at 6.2%. During fermentation, the antioxidant capacity of beans gradually reduced. The fermenting of cocoa beans had a significant effect on the quality formation. The findings of this study constitute a basis for further investigations on the quality formation of cocoa during fermentation. [ABSTRACT FROM AUTHOR]

Summary: The adaptive robust control problem for the speed and tension system of reversible cold strip rolling mill is studied based on the Hamilton theory in this paper. First, the dissipative Hamilton model of the rolling mill system's speed and tension outside loop is built through pre‐feedback control, and then, dissipative Hamilton controllers are designed by utilizing the interconnection and damping assignment and the energy shaping method. Second, in order to realize tensiometer‐free control and adaptive robust control for the perturbation parameters and load disturbance, full‐order state observers and adaptive robust controllers are designed for the rolling mill system's speed and tension outside loop by using the "extended system + feedback" method. Third, robust controllers for the rolling mill system's current inside loop are designed based on the cascade control thought, so as to realize the tracking control for the speed and tension of reversible cold strip rolling mill. Theoretical analyses show that the resulting closed‐loop system is stable. Finally, simulation research is carried out on the speed and tension system of a 1422‐mm reversible cold strip rolling mill, and simulation results verify the validity of the proposed control strategy in comparison with the decentralized overlapping control strategy. [ABSTRACT FROM AUTHOR]

Abstract: To weaken the influences of uncertainties and system coupling items on the coordinated tracking control performance of the speed and tension system of a reversible cold strip rolling mill, a control strategy is proposed based on nonlinear disturbance observers (NDOs), dynamic surface backstepping control, and neural network adaptive approximation. First, the transformation form of the system model is given, and then NDOs are developed to counteract the unmatched uncertainties. Next, controllers for the speed and tension system are presented by combining backstepping with dynamic surface control. Again, the neural network adaptive method is used to approximate the matched uncertainties of the system, and the approximation values are introduced into the designed controllers for compensation. Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using the actual data, and the results show the validity of the proposed control strategy in comparison with the decentralized overlapping control strategy. [ABSTRACT FROM AUTHOR]

A decentralized prescribed performance adaptive tracking control problem is investigated for Markovian jump uncertain nonlinear interconnected large-scale systems. The considered interconnected large-scale systems contain unknown nonlinear uncertainties, unknown control gains, actuator saturation, and Markovian jump signals, and the Markovian jump subsystems are in the form of triangular structure. First, by defining a novel state transformation with the performance function, the prescribed performance control problem is transformed to stabilization problem. Then, introducing an intermediate control signal into the control design, employing neural network to approximate the unknown composite nonlinear function, and based on the framework of the backstepping control design and adaptive estimation method, a corresponding decentralized prescribed performance adaptive tracking controller is designed. It is proved that all the signals in the closed-loop system are bounded, and the prescribed tracking performances are guaranteed. A numerical example is provided to illustrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]