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For a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell (FC), a battery (BAT) and an ultracapacitor (UC), an improved convex-optimization-based energy management strategy (EMS) is proposed in this article. To protect the fuel cell and the battery from peak power, an adaptive fuzzy filter is used to complete the frequency decoupling of the power demand. Subsequently, an adaptive equivalent consumption minimization strategy (A-ECMS) is introduced to improve fuel economy. To improve the optimality of the EMS obtained, an improved convex optimization method is utilized by introducing a slack variable and a barrier function. Finally, based on experimental data and an improved convex optimization algorithm, the optimal EMS is calculated and verified by a series of simulations under different driving cycles. Simulation results confirm that, compared with the traditional ECMS-based EMS, the proposed EMS can improve fuel economy and achieve lower power fluctuation and optimal FC efficiency for the FCHEV. [ABSTRACT FROM AUTHOR]

In this paper, the finite‐time adaptive fuzzy tracking control issue for stochastic nonlinear systems with time‐delay via output‐feedback is studied. Firstly, a state observer and fuzzy logic systems with infinite approximation capability are utilized to estimate the unmeasured states and identify unknown nonlinear characteristics, respectively. Then, a finite‐time adaptive fuzzy tracking controller is devised by the backstepping technology. It is shown that the presented control scheme can ensure the finite‐time square stability of the stochastic nonlinear systems with time‐delay, that is, the output tracking error converges to a sufficiently small neighborhood around the origin in finite‐time, and all signals of the closed‐loop system are bounded. Finally, the effectiveness of the presented control strategy is illustrated via two simulation examples. [ABSTRACT FROM AUTHOR]

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 $\%$. [ABSTRACT FROM AUTHOR]

Insecticidal lamps Internet of things (ILs-IoTs) has attracted considerable attention for its applications in pest control to achieve green agriculture. However, ILs-IoTs cannot provide a perfect solution to the migratory pest outbreak if the ILs are fixed on the ground. In this paper, we embed ILs in unmanned aerial vehicles (UAVs) as the mobile nodes, which can be rapidly landed on the ground to kill agricultural pests, and the Internet of UAVs (IoUAV) is introduced to extend the application of ILs-IoTs. To take full advantage of the IL-IoUAVs, we formulate the problem of target-barrier coverage and investigate how to minimise the number of IL-UAVs in constructing the target-barrier coverage. The $\epsilon$ -target-barrier coverage is introduced utilizing the realistic probabilistic sensing model of IL-UAVs, based on which we study how to guarantee the $\epsilon$ -target-barrier coverage while minimizing the number of IL-UAVs needed. The problem is solved by an optimal algorithm to merge multiple target-barriers. Evaluation results show the efficiency of our designed algorithms for constructing $\epsilon$ -target-barrier coverage. [ABSTRACT FROM AUTHOR]

Summary: This article investigated the adaptive backstepping tracking control for a class of pure‐feedback systems with input delay and full‐state constraints. With the help of mean value theorem, the system is transformed into strict‐feedback one. By introducing the Pade approximation method, the effect of input delay was compensated. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Furthermore, in order to reduce the computational burden by introducing backstepping design technique, dynamic surface control technique was employed. In addition, the number of the adaptive parameters that should be updated online was also reduced. By utilizing the barrier Lyapunov function, the closed‐loop nonlinear system is guaranteed to be semi‐globally ultimately uniformly bounded. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy. [ABSTRACT FROM AUTHOR]

The common dual-pulse-width-modulation converter adopts the independent method to control the rectifier and inverter, which has the defects including direct current voltage fluctuation, slow system response and so on. A coordinate strategy of dual-pulse-width modulation based on direct power control with load power feed-forward is proposed in this paper. The fixed switching frequency method with space vector pulse width modulation is provided to overcome the instability of power devices, caused by the hysteresis comparators in the conventional direct power control. With the analysis on the dynamic performance of load power feed-forward coordination, the principle of load power feed-forward is introduced to control the dual-pulse-width-modulation converter. According to the state variable of the alternating current motor, the load power is estimated to construct the feed-forward channel. The experimental results show that the proposed coordinate control method has rapid response, direct current bus voltage fluctuation minimization, lower capacity and unit power factor. [ABSTRACT FROM AUTHOR]

Summary: With the analysis on the drawbacks of the traditional direct power control for three‐phase pulse width modulation (PWM) rectifier, including the unfixed switching frequency, the higher grid‐side current harmonic component, and the longer system setting time, one method of the model predictive direct power control based on error correction for three‐phase voltage source PWM rectifier is proposed in this paper. Based on instantaneous power theory, the PWM rectifier model predictive objective function of is built. Its partial derivative is calculated to predict the variations of active and reactive power in the next sampling period and achieve the tracking control for the given power of the next period. The second‐order Lagrange interpolation method is introduced to correct the predict power to achieve the minimum error of active and reactive power between actual value and the predicted value. Instead of switch table, space vector PWM is adopted to drive the power switch of rectifier. This method simplifies power control inner‐loop structure and is able to achieve the fixed switching frequency. The experimental results show that this method has better dynamic and static performance; the system is not sensitive to the change of inductance parameters; it can reduce the total harmonic distortion of grid current effectively and improve the power factor. [ABSTRACT FROM AUTHOR]

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan. • Propose a frequency decoupling strategy based on fuzzy control to separate required power into three frequency ranges. • Optimize membership functions of fuzzy controllers using GA. • Improve fuel economy while reducing fluctuation of fuel cell to extend its lifespan. • Confirm the effectiveness of the proposed EMS by simulation and experiment bench results. [ABSTRACT FROM AUTHOR]

This paper investigates fixed-time attitude tracking maneuver of rigid spacecraft in the presence of actuator faults, external disturbances, and inertia uncertainties. First, a fast finite-time stable system is investigated in the sense of fixed-time concept. Then, a nonlinear observer is designed to estimate the information of the lumped uncertainties, and this observer, integral sliding mode and geometric homogeneity are used to formulate one attitude controller, which can guarantee attitude tracking errors can be stabilized to the origin. Second, another adaptive controller is presented to steer attitude tracking errors to the equilibrium point. Lyapunov techniques are implemented to ensure the fixed-time stability of the closed-loop system. Finally, numerical simulations are carried out to demonstrate the performance of the proposed controllers. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper. • Reduce stress on battery and fuel cell to lift power performances and fuel economy. • Adaptive low-pass filter to allocate peak power and braking energy to supercapacitor. • Minimizing hydrogen consumption while improving the working efficiency of fuel cell. [ABSTRACT FROM AUTHOR]

During the operation of speed-sensorless control system for induction motor, the stator and rotor resistance varies greatly with the change of temperature and the frequency of the rotor side, which affects the estimation of the stator flux and leads to the low accuracy of the speed estimation. A speed-sensorless vector control method based on parameters identification with the full-order adaptive state observer is proposed in this paper. In the model reference adaptive system of AC motor, the stator resistance and rotor flux are assigned as state variables to build the reference model, and a full-order flux observer is introduced to adjustable model. Lyapunov theory and Popov superstability theory are used to deduce the speed and rotor resistance adaptive rate. The feedback gain matrix is simplified to speed up the convergence rate of the system. The estimation values of speed and rotor resistance are taken as the proportional integral form, so that an interactive model reference adaptive system is constructed by speed and rotor resistance identification. While observing the rotor flux, it can not only ensure the accuracy of the reference model but also eliminate the disadvantages of the voltage model with integral terms, and the rotor speed can be estimated at the same time. The experimental results show that the accurate performance of speed and flux identification can meet the requirements of application; the proposed control method with the identification of speed and rotor resistance has little fluctuations phenomenon on motor torque in low speed and achieves better performance. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the passivity problem for neural networks with leakage delay and time-varying transmission delay. Using some novel Lyapunov-Krasovskii (L-K) functionals, and combining with the Wirtinger integral inequality, some less conservative delay-dependent passivity conditions are proposed in terms of linear matrix inequalities (LMIs). Different from the existing approaches, the model transformation is abandoned in this paper, and the proposed L-K functionals incorporate the interconnected terms related to both the leakage delay and the time-varying delay. Numerical examples show that the obtained conditions are less conservative. [ABSTRACT FROM AUTHOR]

Driving behavior recognition has attracted wide attention as it can act as an important reference input of many vehicle intelligent control systems. In this paper, a real-time recognition of driver's longitudinal driving behavior is investigated by proposing a hybrid adaptive pattern recognition method. Firstly, a framework of integrated behavior recognition model is established consisting of two sub models to cluster and label the sample data, respectively. Secondly, a new fast high-stability clustering method is proposed to solve the problem of clustering samples with non-negligible noise. And the test results show the clustering process can be completed within a short time lower than 0.1 s with different number of cluster centers. Then, support vector machine and artificial neural network are employed and trained to construct a heuristic self-labeling approach to label the clustered sample automatically in real time with high accuracy (92.9%) under an experimental driving cycle generated from our real-vehicle test bench. Subsequently, the two established modules are integrated and offline trained by 96 thousand historical data, and employed to a 400-second-long online application under a smoothly-varying driving condition, and three further applications under extreme driving cycles with about 1000 s, respectively. Simulation results show that the proposed integrated model is capable of eliminating the interference of noise, and has a relatively stable performance of recognition for different driving cycles with different driving behaviors (92.7% of overall performance for commonly cycles, and greater than 91.2% for extreme cycles), enhancing the capacity for online application. [ABSTRACT FROM AUTHOR]

With the rapid development and wide application of cloud computing and 5G communication, the number of mobile users is increasing rapidly, meaning that cloud storage services are receiving more and more attention. The equality test technology of retrievable encrypted data has become a hot research topic among scholars in recent years. In view of the problem of offline keyword-guessing attacks (KGAs) caused by collusion between internal servers and users, a public key encryption with equality test scheme (RKGA-CET) with higher security against KGAs is proposed. Based on the assumed difficulty of the discrete logarithm problem (DLP) and the properties of bilinear mapping, a specific encryption algorithm that encrypts the keyword twice is designed. In the first encryption stage, we convert the keyword according to the property of isomorphism of a finite field. In the second encryption stage, we encrypt the converted keyword vector and embed the user's private key, and then perform the equality test. The algorithm ensures that the adversary cannot generate legal ciphertexts and implement KGAs when the secondary server is offline. At the same time, the algorithm also supports two authorization modes, in which case users can flexibly choose the corresponding authorization mode according to their own needs. Performance analysis shows that this scheme has overall superiority compared with other similar ones. [ABSTRACT FROM AUTHOR]

A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed for tackling high-dimensional state-action space, and finding a trade-off between global learning and real-time implementation. The power splitting policy based on experimental data is obtained by using reinforcement learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption economy. Simulation results confirm that, compared with existing learning algorithms and optimization methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV. • Decouple power demand of FCHEV based on fuzzy filter to improve RL algorithm. • Propose a novel algorithm to achieve optimal EMS for splitting power of sources. • Combine ECMS to refine algorithm to optimize fuel consumption and fuel cell lifespan. • Utilize experimental data to confirm effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A reliable energy management strategy (EMS) is of utmost importance in enhancing the fuel economy of fuel cell hybrid electric vehicles. However, correlation between fuel cell output power and temperature pose an urgent challenge of effectively integrating fuel cell thermal regulation with energy management. In this paper, an improved deep deterministic policy gradient (DDPG) EMS based on fixed time active disturbance rejection controller (FTADRC) is proposed. The proposed method not only achieves precise tracking of the optimal operating temperature in temperature regulation of fuel cell, but also seamlessly integrates thermal management with EMS. Specifically, through fuel cell thermal regulation experiments, the optimal operating temperature of the fuel cell is determined by fitting the relationship between temperature and fuel cell power. Then, a FTADRC temperature controller is designed to track the optimal operating temperature. Meanwhile, an integrated framework for EMS and fuel cell thermal regulation is developed, where the temperature difference and compensation power are considered in the DDPG state space and reward function. In order to accelerate the learning process of DDPG strategy, a temporal difference error network parameter adaptive update mechanism is proposed. Finally, simulations and experiments are conducted on three different driving cycles, where the obtained real vehicle test results show that compared with the improved DDPG strategy based on traditional thermal regulation, this method reduces hydrogen fuel consumption by 15.79% and improves average fuel cell efficiency by 1.87%. In addition, compared to traditional DDPG algorithms, the proposed DDPG algorithm reduces training time by 24%. [ABSTRACT FROM AUTHOR]

This paper investigates a high-order disturbance observer-based safe tracking control scheme for a class of uncertain multiple-input and multiple-output systems under time-varying full state constraints and disturbances. To achieve the safe tracking objective, a boundary protection algorithm is introduced to generate new safe desired signals which are within corresponding state constraints. An improved second-order dynamic surface control technology is developed to deal with the piecewise differentiability of safe desired signals and the phenomenon of repeatedly differentiation, simultaneously. To handle the negative effects of system uncertainties and obtain better estimation effect of high order time-varying disturbances, the radial basis function neural networks and high-order disturbance observer methods are developed. The safety and performance of the closed-loop nonlinear system under the proposed control scheme have been rigorous proved and discussed by Lyapunov stability analysis. Finally, a two-link manipulator model has been given as an example, and the numerical simulations are given to express the availability of the proposed controller. • Different with the high-gain principle of common constrained control methods, a boundary protection algorithm (BPA) has been proposed to calculate new safe desired signals, which can solve the full-state constrained control problem with the time-varying and unpredicted state constraints. • A novel auxiliary system and a second-order filter (SOF) technique have been developed to solve the problem caused by BPA. Comparing with conventional filter in dynamic surface control [13], this SOF can reach the unbiased estimation of the time derivative of the input signal. • The HODO-based safe tracking control scheme is able to realize the safety and convergence of the higher-order, under-actuated nonlinear system under high frequency disturbances proved by rigorous Lyapunov analysis, and the restricted conditions have been strictly discussed. [ABSTRACT FROM AUTHOR]

In this article, the problem of adaptive fuzzy control for stochastic high-order nonlinear systems with full-state constraints of the strict-feedback structure was investigated. The unknown nonlinear functions are approximated by using fuzzy logic systems (FLSs) at each step. By introducing the barrier Lyapunov functional candidate, a novel adaptive fuzzy backstepping control strategy is proposed to solve the control problem of stochastic nonlinear systems with full-state constraints. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The neural‐point‐clamped (NPC) three‐level inverters exhibit a midpoint potential unbalance problem. With the analysis on the midpoint potential fluctuation, its voltage vectors are discussed, and the mathematical model of the midpoint current in each sector is established. Based on virtual space vector pulse‐width modulation (VSPWM) calculated in the gh coordinate, a balance control method is proposed to compensate the midpoint potential. The virtual medium vector and voltage adjustment factor are introduced. Different positive and negative short vectors are distributed to different adjustable parameters to improve the influence of short vectors on the midpoint potential. The experimental results show that the proposed method balances the midpoint potential effectively, and VSVPWM with the gh coordinate can reduce the complexity of the calculation. [ABSTRACT FROM AUTHOR]

In this paper, the issue of finite‐time prescribed performance control (PPC) is addressed for stochastic nonlinear systems with the effect of dead‐zone. Fuzzy logic systems are utilized for identifying unknown nonlinear characteristics. A model consisting of linear and disturbance‐like terms is constructed to approximate the nonlinearity of dead‐zone. Meanwhile, a state observer is employed to estimate unmeasured states. To improve the convergence rate, a finite‐time performance function is introduced, which transforms the tracking error of original system to error of converted system, thus prescribing the transient and steady‐state performance of the system. Eventually, based on backstepping technique, a finite‐time PPC strategy is proposed, which ensures the boundedness of all signals in closed‐loop system. Simultaneously, the tracking error converges to a prescribed region in finite‐time. Finally, two simulation examples are provided to verify the effectiveness of presented control strategy. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear systems with multiple uncertainties is considered by using a composite operator-based coprime factorisation method. Firstly, as for the exogenous external disturbance, the disturbance model is considered and the disturbance output is guaranteed to be bounded. Meantime, the adverse effect resulting from it is transformed to an equivalent effect on the stable part of the systems. By using operator-based coprime factorisation method, a feasible framework on multiple uncertainties is obtained. Then, the obtained equivalent effects resulting from the modelled disturbance and adverse effect from the internal perturbation are unified and addressed. Thirdly, sufficient conditions on guaranteeing robust stability of the considered nonlinear systems are considered by using robust coprime factorisation method, for relaxing computation burden on Bezout identity and avoiding requirement on knowing the perturbation signal. Based on the proposed conditions, two controllers are designed and the robust stability of the considered systems is proved. Finally, simulation results are shown to explain the proposed design scheme and confirm its effectiveness of this paper. [ABSTRACT FROM AUTHOR]

For tracking of multiple extended targets with unknown and varying target number, the measurements partitioning is indispensable and plays an important role in achieving good performance. However, the number of possible partitions grows very fast with the increasing of total number of measurements, which causes heavy computational burden. Thus this Letter proposes a fast measurement partitioning algorithm, in which the target predictive information is explicitly considered and used to determine the clustering centres for measurement partitioning. To guarantee its robustness, the target predictions are further modified when the target number is overestimated. This flexible strategy reduces the dependence on original predictive information and significantly improves the accuracy of partitioning results. Besides, the proposed algorithm greatly reduces the partition number by fully considering possible changes of the number of newborn targets and spawned targets. It not only eliminates unreasonable and redundant measurement partitions for tracking performance improvement, but also fits well with the tracking of newborn targets and spawned targets. Simulation results verify the benefit of what the authors proposed. [ABSTRACT FROM AUTHOR]

As for fuel cell hybrid electric vehicles (FCHEV) powered by fuel cell, battery, and ultracapacitor, its complex topology structure and the variable terrain environment bring challenges to the energy management for power sources lifespan and fuel economy. In this paper, a terrain information-involved energy management strategy (EMS) obtained by an improved deep reinforcement learning (DRL) algorithm is proposed to distribute reasonably the power demand for FCHEV. Specially, an action-value-based adaptive noise and an action-screening-mechanism-based priority experience replay are designed for efficient strategy learning with the adopted algorithm. To protect fuel cell and battery from peak power, a hierarchical energy management framework is established by introducing an adaptive fuzzy filter. Then, an equivalent consumption minimization strategy based on multi-objective optimization is constructed and used as the reward of the improved algorithm to achieve the minimum fuel consumption. Meanwhile, based on historical experimental data, the terrain-information-considered transition probability matrix is obtained and applied to the algorithm to reduce computational load and get the optimal EMS. Finally, the effectiveness of the proposed EMS is verified by a series of comprehensive simulations under different driving cycles. The simulation results show that the hierarchical EMS framework can better protect the service life of fuel cell and battery. In addition, compared with the EMS without considering terrain information, the incorporation of terrain information in EMS can enhance the operational efficiency of fuel cell, improve fuel economy by about 8%, and can reach the dynamic programming benchmark level of 89.5%. [ABSTRACT FROM AUTHOR]

The problem of designing state and adaptive disturbance observers for a class of nonlinear descriptor systems with the disturbance generated by an unknown exogenous system is addressed in this paper. Especially, the nonlinear terms satisfy incremental quadratic constraints parameterized by a bunch of multiplier matrices, which can represent various classes of nonlinearities. For three different cases in the output equation, the state and adaptive disturbance observers are proposed. Some sufficient conditions formulated in terms of linear matrix inequalities and algebraic constraints are derived to guarantee that state and disturbance error systems, simultaneously, are asymptotically stable with H∞$$ {H}_{\infty } $$ performance level. The availability of proposed observers is validated by two practical examples. [ABSTRACT FROM AUTHOR]

This paper investigates the positive impulsive observer design of fractional-order positive switched systems with mode-dependent average dwell time (MDADT). By using the MDADT approach and the Lyapunov–Krasovskii functional technique, sufficient conditions are obtained to ensure the error system is globally exponentially stable. An algorithm is proposed to design the observer gain matrices via linear programming. Finally, two examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A hierarchical energy management framework is employed to reduce the dimensionality of the action-state space. • The health condition of BAT is involved in the established A-MOECMS-based optimization framework. • An action-value-based adaptive greedy noise and heuristic experience replay are introduced to improve the optimization ability of algorithm. To acquire an optimal way to solve the energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs), most of existing research focuses too much on the protection of fuel cell, while the degree of degradation of battery as an internal influence factor also plays an important role in EMS. In this paper, battery-degradation-involved hierarchical energy management framework utilizing an improved deep deterministic policy gradient (DDPG) algorithm is proposed for gaining the optimal EMS of FCHEV with three power sources. Firstly, to protect fuel cell and battery against the peak power, an adaptive fuzzy filter is employed to complete frequency-based decoupling of power demand for achieving the stratification of power. Then, the degradation model of battery is adopted according to the available data types of our test bench, and an adaptive multi-objective equivalent consumption minimization strategy model is constructed and solved by an improved DDPG-based algorithm. Finally, the simulation results show that, compared with the traditional DDPG, the proposed EMS can enhance the efficiency of fuel cell by 2.02% on average, and reduce the performance degradation of battery by 14.4% on average. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive fuzzy fixed-time control strategy is proposed for stochastic nonstrict nonlinear systems with Bouc–Wen type of hysteresis input and unmeasured state variables. The Bouc–Wen model is recognized as one of the most intractable mechanical hysteresis models. The established state observer is used to assess unmeasurable states, and the fuzzy logic systems are utilized to approximate the uncertain nonlinear functions in the system. Based on the state observer and backstepping method, a fixed-time controller is recursively designed to ensure that all closed-loop signals of the system are bounded and the tracking error converges to a small neighborhood of the origin within a fixed-time. Finally, the effectiveness of the control strategy is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of finite-time control of uncertain fractional-order positive impulsive switched systems (UFOPISS) via mode-dependent average dwell time (MDADT). The uncertainties refer to interval and polytopic uncertainties. Firstly, the proof of the positivity of UFOPISS is given. By constructing linear copositive Lyapunov functions, the finite-time stability (FTS) of autonomous system with MDADT is studied. Then, state feedback controllers are designed to guarantee the FTS of the resulting closed-loop system with interval and polytopic uncertainties, respectively. All presented conditions can be easily solved by linear programming. Finally, a fractional-order circuit model is employed to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the development of image segmentation technology, image context information plays an increasingly important role in semantic segmentation. However, due to the complexity of context information in different feature maps, simple context capture operations can easily cause context information omission. Rich context information can better classify categories and improve the quality of image segmentation. On the contrary, poor context information will lead to blurred image category segmentation and an incomplete target edge. In order to capture rich context information as completely as possible, we constructed a Multi-Pooling Context Network (MPCNet), which is a multi-pool contextual network for the semantic segmentation of images. Specifically, we first proposed the Pooling Context Aggregation Module to capture the deep context information of the image by processing the information between the space, channel, and pixel of the image. At the same time, the Spatial Context Module was constructed to capture the detailed spatial context of images at different stages of the network. The whole network structure adopted the form of codec to better extract image context. Finally, we performed extensive experiments on three semantic segmentation datasets (Cityscapes, ADE20K, and PASCAL VOC2012 datasets), which fully proved that our proposed network effectively alleviated the lack of context extraction and verified the effectiveness of the network. [ABSTRACT FROM AUTHOR]

In this paper, the H ∞ synchronization problem for descriptor systems with nonlinearities satisfying incremental quadratic constraints under both external and stochastic disturbances is studied. The incremental quadratic constraints can include Lipschitz, one-side Lipschitz, sector-bounded, nondecreasing nonlinear functions, etc. Then, a periodically intermittent controller is designed and several sufficient conditions are obtained to make the drive and response systems synchronized with H ∞ disturbance attenuation level by exploiting the Lyapunov stability theory and the It o ^ formula. Finally, two examples are given to illustrate the desired performance of the designed controller. [ABSTRACT FROM AUTHOR]

This paper is concerned with disturbance observer-based input-output finite-time control of a class of nonlinear systems with one-sided Lipschitz condition, as well as multiple disturbances. Firstly, a disturbance observer is constructed to estimate the disturbance generated by an exogenous system. Secondly, by integrating the estimation of disturbance with a classical state feedback control law, a composite control law is designed and sufficient conditions for input-output finite-time stability (IO-FTS) of the closed-loop system are attained. Such conditions can be converted into linear matrix inequalities (LMIs). Finally, two examples are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded as an extended one with object extension, e.g., its shape may be described by the star-convex random hypersurface model. This model is usually specified by a one-dimensional radial function. However, the divergence of the shape estimation and a high error of the kinematic state estimation are likely to occur when an extended object maneuvers. This is because the radial function may take a negative value after Fourier series expansion, which leads to unpredictable estimation results. Unfortunately, the model itself is unable to solve this problem via the subsequent iterations. In this paper, we proposed a modified shape estimation approach to track an extended object with a star-convex random hypersurface model based on minimum cosine distance. Both the extension state and kinematic state at the current time are reinitialized once the radial function takes a negative value. Moreover, a mathematical model was constructed by using the principle of minimum cosine distance, so as to obtain more reasonable weight distribution coefficients for the correction of the extension state. Simulation results in different scenarios demonstrated the effectiveness of the proposed tracking approach. [ABSTRACT FROM AUTHOR]

This paper proposes the adaptive quantized controller design for the synchronization of a class of fractional-order nonlinear systems satisfying incremental quadratic constraints governed by an incremental multiplier matrix. The incremental quadratic constraints can describe many commonly encountered nonlinearities in existing literature. The adaptive quantized controller are designed and formulated in terms of matrix inequalities to make the error system asymptotically stable. Meanwhile, the sufficient conditions can be obtained via solving linear matrix inequalities. Moreover, an algorithm is presented to illustrate the steps of designing adaptive quantized controllers. Finally, examples about fractional-order Lorenz chaotic system and fractional-order Bloch system are provided to illustrate the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

Vapor equilibrium transport method is used to prepare near stoichiometric lithium tantalate crystals with high quality. However, it is difficult to prepare large-thickness crystals by this method due to its low solid diffusion rate. In this paper, based on the diffusion mechanism of lithium tantalate crystal, the diffusion of lithium tantalate crystal was carried out by asymmetric diffusion process with lithium rich atmosphere on one side of the wafer and congruent composition atmosphere on the other side. The composition and domain inversion field were characterized. The results show that the asymmetric diffusion technique provides a channel for the diffusion of anti-tantalum ion and increases the diffusion rate of anti-tantalum ion and lithium ion in the crystal, which is beneficial to the preparation of large-thickness near stoichiometric lithium tantalate crystal. [ABSTRACT FROM AUTHOR]

This paper considers the impulsive functional observer design problem for fractional-order nonlinear systems satisfying incremental quadratic constraints. The incremental quadratic constraints can describe many commonly encountered nonlinearities in the existing literature. Firstly, a fractional-order nonlinear system with incremental quadratic constraints and output nonlinearities is presented and the definition of incremental quadratic constraints is introduced. Secondly, an impulsive functional observer is designed to guarantee that the error system is exponentially stable. Thirdly, based on matrix partition, an approach to obtain the gains of the observer is proposed. Meanwhile, the sufficient conditions can be obtained via solving linear matrix inequalities. Subsequently, an algorithm is given to illustrate the steps of impulsive functional observer design. Finally, an example about fractional-order chaotic system is provided to illustrate the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model set used. However, the existing model set design methods still cannot offer higher tracking performance due to the complex maneuverability of the target. The current best model augmentation (BMA) algorithm is an efficient and universal model set design method, but it cannot adapt well to complex maneuvering situations because of its fixed basic and candidate model set. Hence, this paper proposes a modified BMA algorithm for VSMM based on adaptive grid, it can make full use of the previous information to realize an adaptation and time variation of the model set. Overall, two different scenarios were considered, wherein the digital simulation shows that the proposed method can provide a more reliable estimation of the kinematic state and shape of the extended target. [ABSTRACT FROM AUTHOR]

Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

During the actual flight of unmanned helicopters, there are widespread output constraints, system uncertainties and external disturbances, which bring significant threats to their safe flight. To keep the output constraints from violating predefined boundaries, the barrier Lyapunov function (BLF) is utilized in this paper. The system uncertainties are estimated by the fuzzy logic system (FLS). The compound disturbances composed of the external disturbances and the approximation errors caused by FLS are approximated by the fixed-time disturbance observers (FTDOs). According to the analysis of FTDOs and fixed-time stability theory, the observer errors and state errors converge to a region of the origin in fixed-time, and the appropriate parameters of FTDOs and controller are designed to ensure that the disturbance convergence time is faster than the one of the proposed controller, which further improves the stability of the system. Moreover, the convergence time of the FTDOs FTDOs and the proposed approach only depends on the design parameters under any initial conditions with constrained outputs. The performance of the proposed control approach is verified through the simulation results. [ABSTRACT FROM AUTHOR]

The performance of various density functional theories (B3LYP, B3PW91, B3P86, B1LYP, BMK, MPWB1K, PBE0, and MPWB95) was examined for calculating N-NO2 bond dissociation energies (BDEs) of 10 N-nitroacylamide compounds. The CBS-4M method was also used. By comparing the calculated results with the experimental values, it was observed that B1LYP/6-31G** and B3LYP/6-31+G** provided accurate BDEs. Especially, B3LYP/6-31+G** was recommended because of its smaller maximum absolute deviation. Further, substituent effects based on the B3LYP/6-31+G** method were analyzed. The result shows that an electron-donating group increases the BDE of the parent C6H5-CON(CH3)NO2, while an electron-withdrawing group decreases the BDE of the parent C6H5-CON(CH3)NO2. Subsequently, the BDEs of the other N-nitroacylamindes were estimated. [ABSTRACT FROM AUTHOR]

This paper considers the problem of guaranteed cost and finite-time event-triggered control of fractional-order switched systems. Firstly, an event-triggered scheme including both the information of current state and an exponential decay function is proposed, and a novel cost function that adopts the characteristics of fractional-order integration is presented. Secondly, some sufficient conditions are derived to guarantee that the corresponding closed-loop system is finite-time stable with a certain cost upper bound, using multiple Lyapunov functions and average dwell time approach. Meanwhile, the event-triggered parameters and state feedback gains are simultaneously obtained via solving linear matrix inequalities. Moreover, Zeno behavior does not exist by finding a positive lower bound of the triggered interval. Finally, an example about fractional-order switched electrical circuit is provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]