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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, 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]

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]

Energy management strategy (EMS) plays a crucial role in improving the fuel economy of fuel cell hybrid electric vehicles (FCHEV). In this paper, an alternating direction method of multipliers-based distributed interior point based EMS is proposed for FCHEV equipped with fuel cell (FC), battery (BAT), and ultracapacitor to reduce hydrogen consumption, improve fuel economy and extend the service life of energy sources. To mitigate the impact of peak power on FC and BAT, an adaptive fuzzy filter is employed to decouple the power demand frequencies. Subsequently, the adaptive equivalent consumption minimization strategy (A-ECMS) is proposed to enhance fuel economy. Since A-ECMS is a common local optimization problem characterized by non-convexity, an enhanced convex optimization theory is proposed to convert the non-convex problem into a convex one. The convex problem is then solved using an alternating direction method of multipliers-based distributed interior point method based EMS for FCHEV to enhance computational efficiency and convergence performance. Simulation results conclusively illustrate that the proposed EMS enhances the fuel economy of FCHEV, reduces power fluctuations of FC, and improves computational efficiency when compared to Convex optimization and the interior point method. • Applying adaptive fuzzy control filter optimizes energy management of fuel cell hybrid electric vehicle by allocating high frequency to ultracapacitor and low frequency to fuel cell and battery. • Proposing adaptive equivalent consumption minimization strategy enhances battery life, reduces hydrogen consumption. • Adopting alternating direction method of multipliers-based distributed interior point method for energy management strategy reduces computation burden, enhances convergence, computational efficiency. [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]

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 fuel cell/battery/ultracapacitor hybrid electric vehicles (FCHEV), the complex topology and terrain information pose challenges to fuel cell efficiency, power sources lifespan, and fuel economy. In this paper, a hierarchical energy management strategy (EMS) considering predicted terrain information is constructed to optimize the demand power allocation of FCHEV based on twin delayed deep deterministic policy gradient (TD3). Firstly, to improve the fuel economy of FCHEV, an adaptive fuzzy filter-based upper-level frequency separation is designed considering negative power. Secondly, to achieve the minimum fuel consumption, a TD3-based lower-level EMS is designed, where the terrain information is predicted through multi long short-term memory network and considered in the TD3 framework. The optimal state of charge obtained by dynamic programming (DP) based on the demand power and predicted terrain information, is considered in the reward function of TD3, as well as the optimal experience solved by DP based on historical road data is integrated into the designed hybrid experience replay. Finally, the effectiveness and optimality of the TD3-based EMS considering predicted terrain information are verified by a series of comprehensive simulations under different driving cycles. The simulation results under CYC_SC03 and test driving cycles indicate that compared to traditional TD3-based EMS, the fuel economy is increased by 33.3% and 27.5%, respectively, and can reach the benchmark level of 80% and 80.9% of DP, respectively. • Adaptive fuzzy filter allocates peak power and braking energy to ultracapacitors. • Considering predicted terrain information to extend lifespan of fuel cell and battery. • Considering dynamic programming to improve efficiency of offline network learning. • Minimizing equivalent fuel consumption while reducing state of charge fluctuation of battery. [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]

• 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]

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, extended operator-based robust right coprime factorisation is investigated for dealing with a class of nonlinear systems with unknown bounded perturbations. First, a new kind of operator is introduced, by which operator-based right coprime factorisation approach is extended to consider the perturbed nonlinear systems. By regulating the exponent of the proposed operator, a broader class of nonlinear systems can be handled using the extended right coprime factorisation approach. Second, for guaranteeing robust stability of the perturbed nonlinear systems, feasible design schemes based on some sufficient conditions are discussed, which can reduce complicated calculation in control processing using a different unimodular operator. Finally, a simulation example is involved to illustrate the proposed design scheme for confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a class of multi-input–multi-output (MIMO) nonlinear systems with uncertainties is considered by using operator-based right coprime factorisation. First, based on proposed quotient operators, coupling effect of the MIMO nonlinear systems is discussed. Second, based on the operator-based right coprime factorisation approach, sufficient conditions for guaranteeing robust stability of the MIMO nonlinear systems with uncertainties are proposed by using a new unimodular operator. Finally, a simulation example is shown to illustrate the proposed design scheme for the MIMO nonlinear systems with uncertainties. [ABSTRACT FROM AUTHOR]

In this study, a realisation to robust right coprime factorisation and robust stability of non‐linear systems with unknown bounded perturbations are investigated based on inner product of Hilbert spaces. In detail, first, a feasible framework based on inner product is proposed to study robust right factorisation of the perturbed non‐linear systems, which provides fundamental for factorising the systems and guaranteeing robust stability. Second, a condition based on adjoint operators of Hilbert spaces is given for the non‐linear systems with unknown bounded perturbations, according to which a compensator is designed and meanwhile eliminates difficulties in obtaining internal signal of the perturbed non‐linear systems. After that, a realisable design scheme on robust stability is given based on the designed controller and the unimordular property. According to the proposed robust design scheme, the non‐linear systems with unknown bounded perturbations can be handled precisely and effectively. Finally, a simulation example is given to confirm the effectiveness of the proposed methods. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]