In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.
Achieving compliance and flexibility under the premise of ensuring trajectory tracking performance and also reflecting the wearer’s movement intention, has not yet been well solved in the field of prosthesis. The aim of this paper is to provide a compliant, robust, and continuous control scheme for robotic knee prosthesis to solve the contradictory problems of trajectory tracking performance and compliance. The proposed scheme are based on the admittance model and radial basis function (RBF) neural network–enhanced nonsingular fast terminal sliding-mode controller (NFTSMC). The desired trajectory of the prosthetic knee joint is driven by humans and reshaped to reference trajectory by an admittance model, so that the prosthetic leg can reflect the human’s movement intention and being compliant. RBF neural network is introduced to achieve adaptive approximation of unknown models and ensure that the controller does not depend on the mathematical model of the “human-in-the-loop” prosthesis system. A novel NFTSMC was proposed to deal with the influence of ground reaction forces (GRFs) and fitting errors of the RBF neural network, which make the tracking error converge to zero in a finite time. The adaptive law of the RBF neural network is obtained by the Lyapunov method, and the stability and finite-time convergence of the closed-loop system are rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the propose control scheme.
Load-carrying exoskeletons need to cope with load variations, outside disturbances, and other uncertainties. This paper proposes an adaptive trajectory tracking control scheme for the load-carrying exoskeleton. The method is mainly composed of a computed torque controller and a fuzzy cerebellar model articulation controller. The fuzzy cerebellar model articulation controller is used to approximate model inaccuracies and load variations, and the computed torque controller deals with tracking errors. Simulations of an exoskeleton in squatting movements with model parameter changes and load variations are carried out, respectively. The results show a precise tracking response and high uncertainties toleration of the proposed method.
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