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.

We consider the tracking control problem for a class of stochastic nonlinear systems in strict-feedback structure via output feedback signal in this paper. The controlled plant is assumed subject to unknown dead-zone input. By Utilizing the fact that the unknown dead-zone input function can be modeled as a time-varying nonlinear function and a bounded disturbance, and selecting appropriate design parameters, we show that the effect of unknown dead-zone can be compensated. We design a fuzzy state observer via fuzzy logic modelling technique, to estimate the unknown system states. Then, we prove, via Lyapunov stability analysis, that the controlled plant is bounded in probability. In addition, all the signals in the closed-loop system are guaranteed to be globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, to show the effectiveness of the proposed control strategy, a simulation example of one-link manipulator is presented in the simulation section.

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.

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m.