In this paper, an observer-based adaptive control method is investigated for the horizontal trajectory tracking of autonomous underwater vehicles (AUV) with input saturation and system disturbances. Firstly, the desired surge speed and trajectory angle are established, which could decouple the tracking error subsystem and avoid the complex form. Secondly, the input saturation is approximated by a smooth function, and a nonlinear extended states observer (NESO) is designed for estimating system disturbances. Based on the command filtered backstepping technique, which can avoid the explosion caused by the derivative of the virtual control, an observer-based adaptive output feedback control method is developed, and an auxiliary system is applied to compensate for filtered tracking errors, input saturation bias, and observer errors. Finally, simulation results show the proposed method has good robustness in the face of system uncertainties, and the error is nearly 33.3% smaller than that of other control methods when meeting sudden trajectory changes. A good control performance is guaranteed.

In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse scenario, the X-axis, Y-axis, and Z-axis localization errors are 0.129 m, 0.101 m, and 0.154 m, respectively.

Traditional Ultra-wideband (UWB) positioning algorithms cannot achieve ideal positioning results when facing multipath effects and non-line-of-sight (NLOS) factors. In order to improve the accuracy of UWB indoor positioning, this paper proposes an improved grey wolf optimization (GWO) algorithm for 3-D UWB indoor positioning in the NLOS environment. First, the Chan algorithm is used for initial tag positioning. Then, the search area of the GWO algorithm is constructed with the initial positioning result as the center. Next, the GWO algorithm’s optimization accuracy and convergence speed are improved through the improvement of Tent chaotic mapping, nonlinear convergence factor based on cosine function, and dynamic inertia weight factor. Finally, the optimized position of the tag is determined using the improved GWO algorithm. Experimental results show that this algorithm can converge to the global optimal solution faster and achieve higher positioning accuracy in complex experimental environments. Compared with the Chan, ChanTaylor, particle swarm optimization (PSO), GWO, and enhanced grey wolf optimization (AGWO) algorithms, the average positioning accuracy of the proposed algorithm is improved by 62.92%, 66.43%, 45.71%, 40.91%, and 37.76% respectively, demonstrating its high practical value.

Ultra-wideband (UWB) technology has significant application value in indoor autonomous navigation and robot path planning. However, noise interference in complex environments often causes anomalies in UWB positioning data, severely reducing positioning accuracy and stability. A particle filter algorithm (LTPF) based on chaotic sequences and an improved t-distribution is proposed to address this issue. This algorithm applies a linear transformation to the Logistic map to generate chaotic sequences, thereby enhancing particle diversity and distribution. Additionally, it uses standardized residuals to improve the t-distribution and construct an observation likelihood function, thereby reducing the impact of noise points and outlier data. Experimental results show that compared to the PF, EKPF, KGAPF, and SKF-DMM algorithms, the LTPF algorithm achieves average positioning accuracy improvements of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively, effectively enhancing positioning accuracy and stability. Additionally, its computational complexity is equivalent to the PF algorithm, making it a viable method for UWB positioning in complex indoor environments.