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This study presents a novel uncertainty estimation algorithm that aims to enhance the accuracy of a reduced-order dynamic model for a flexible-joint lever arm (FJLA). The model uncertainties are compensated by adding a complementary term to the reduced-order model, upgrading it to the real model. The gyroscope bias in measuring the angular velocity is corrected by fusing the information of encoder/gyroscope. The effective data fusion is provided by adaptive adjustment of the estimator coefficient. The constructed model, with low order but rich content, is reliable for use in the position controller of FJLA, developed by a continuous predictive method. The control system adapts itself to real conditions and is cost-effective due to the use of fewer sensors. The saturation of motor torque, as the control input, is modeled within the structure of a constrained optimization problem solved by Karush–Kuhn–Tucker Theorem. The boundedness of the mean and covariance of tracking error and its derivative are demonstrated by stochastic analysis. The results of simulations and experimental implementations demonstrate the high efficiency of the proposed system in controlling the position of the FJLA under different trajectories. Moreover, the comparative results with the other methods show a great performance for the suggested control system in rejecting disturbances and other uncertainties under saturated control input. [ABSTRACT FROM AUTHOR]

The main challenge of low-cost strap-down inertial navigation systems (SINSs) is time-growing positioning error due to erroneous measurements of micro-electro mechanical system (MEMS)-based inertial sensors. The global navigation satellite system (GNSS) provides drift-free positioning data that can be appropriately utilized to prevent the cumulative error of stand-alone SINS. The primary aim of this research is to enhance the positioning accuracy, performance, and reliability of low-cost SINS/GNSS-integrated navigation system. To attain this, we propose an applied data fusion algorithm for indirect centralized (IC) integrated SINS/GNSS. The proposed data fusion algorithm is based on fuzzy-adaptive constrained estimation filter. Velocity and altitude constraints are embedded in the integration scheme of the proposed SINS/GNSS system to preserve system reliability during abrupt GNSS outage. In an innovative way, the state constraints of altitude are defined based on the measurements of air-data sensors. The state estimation is effectively optimized since the respective states are projected on a constraint surface. Furthermore, a fuzzy type-2 inference system is developed for adaptively changing the covariance matrix of the estimation algorithm. Inertial measurements are used as the input of the fuzzy inference system. Accordingly, the state estimation algorithm is adaptively modified based on the vehicle's maneuvering during the navigation trajectory. The proposed SINS/GNSS system is experimentally assessed through several vehicular tests. The results indicate that not only does the proposed algorithm improve the navigation accuracy, it will also enhance the reliability of the integrated navigation system during GNSS outage. [ABSTRACT FROM AUTHOR]