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.

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 $\%$ .

In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.

The majority of existing energy management strategies (EMSs), merely considering external driving conditions, often allocate demand power in an irrational way, resulting in a waste of energy and a short service life of power sources. Therefore, it is necessary to integrate driving behavior in EMS to reduce the fuel consumption and improve the lifespan of power sources. In this paper, a driving-behavior-aware adaptive deep-reinforcement-learning (DRL) based EMS is proposed for a three-power-source fuel cell hybrid electric vehicle (FCHEV). To fully utilize each power source, a hierarchical power splitting method is adopted by an adaptive fuzzy filter. Then, a high-performance driving behavior recognizer is employed, and Pontryagin’s minimum principle (PMP) method is used to compute the optimal equivalent factor (EF) of each driving behavior. To realize a trade-off between global learning and real-time implementation, an improved multi-learning-space DRL-based algorithm, applying driving-behavior-aware adaptive equivalent consumption minimization strategy (A-ECMS) and soft learning mechanism, is proposed and verified by a series of simulations. Simulation results show that, compared with the benchmark method ECMS, the proposed P-DQL method can reduce the hydrogen consumption by 49.9 $\%$ on average, and the total cost to use by 31.4 $\%$ , showing a promising ability to increase fuel economy and reduce hydrogen consumption and the total cost to use of FCHEV.

This paper investigated the adaptive fuzzy finite-time control problem for a class of high-order stochastic nonlinear systems with a class of exponential type prescribed performance function. It is assumed that the nonlinear functions in the controlled plant are unknown, in which fuzzy logic systems (FLSs) are utilized due to the approximation ability of any unknown continuous functions with arbitrary approximation errors. Based on the FLSs and backstepping design technique, an novel adaptive fuzzy tracking control strategy is proposed to guarantee that the closed-loop nonlinear system is semi-globally finite-time stable in probability via Lyapunov stability theory and It ${\hat o}$ formula. Compared with existing results, the transformed error signal was regarded as a stochastic variable. In addition, the expressions of the first and second-order partial derivatives of the transformed error signals are given in this paper. Finally, a simulation example with different covariance values is given to show the effectiveness of the proposed control strategy.

In this article, the finite-time adaptive fuzzy state-feedback tracking control problem for the pure-feedback system with full state constraints is studied. In order to transform the pure-feedback form into system strict-feedback case, the mean value theorem is introduced. By employing finite-time-stablelike function and state transformation for output tracking error, the output tracking error converges to a predefined set in a fixed finite interval. To tackle the problem of state constraints, integral barrier Lyapunov functions are utilized to guarantee that the state variables remain within the prescribed constraints with feasibility check. Fuzzy logic systems are utilized in this article to approximate nonlinear uncertainties. In addition, all the signals in the system are guaranteed to be semiglobal ultimately uniformly bounded. Finally, two simulation examples are given to show the effectiveness of the proposed control strategy.