New fractional-order LADRC scheme based on a novel filtered-Bode’s ideal transfer function for integer-order systems

In this paper, a new Fractional-Order Linear Active Disturbance Rejection Control scheme (FO-LADRC) is proposed to enhance the robustness against loop gain variations of the standard Active Disturbance Rejection Control (ADRC) in the case of uncertain integer-order systems. A new filtered Bode’s ideal transfer function (F-BITF) is proposed to be used as a reference model in the design approach of the proposed control scheme to ensure the dynamic behavior of the closed-loop BITF to the controlled system. A Fractional-order Extended State Observer (F-ESO) is used in the proposed FO-LADRC structure to approximate the system to be controlled by a filtered fractional-order integrator. The fractional order of the F-ESO is a design parameter to tune to achieve the desired overshoot of the closed-loop step response. For the tuning of FO-LADRC structure, an analytical method is proposed. The performance of the proposed FO-LADRC and the Chen’s et al. FO-ADRC structures are evaluated thorough numerical simulation, and then validated in practice in the case of a Cart-Pendulum. Both the simulation and the experimental results show that the proposed FO-LADRC is able to achieve the desired dynamics of the F-BITF and guarantee the robustness with respect to the controller gain variation and the system parameter uncertainties. The comparative study conducted also reveals that the proposed control scheme is more robust than that of Chen.     

Enhanced modified reliability index approach for efficient and robust reliability-based design optimization

The reliability index approach (RIA) is one of the effective tools for solving the reliability-based design optimization (RBDO) probabilistic model, which models the uncertainties with probability constraints. However, its wide application in engineering is limited due to low efficiency and convergence problems. The RIA-based modified reliability index approach (MRIA) appears to be very robust and accurate than RIA but yields inefficient for the most probable point (MPP) search with highly nonlinear probabilistic constraints. In this study, an enhanced modified reliability index approach (EMRIA) is developed to improve the efficiency and robustness of searching for MPP and is utilized for RBDO. In the EMRIA, an innovative active set using rigorous inequality is applied to construct the region of exploring for MPP, where the unnecessary probabilistic constraint could be eliminated adaptively during the iterative process. Moreover, the double loop strategy (DLS) is integrated into the EMRIA to strengthen the efficiency and robustness of large-scale RBDO problems. Two numerical examples demonstrated that the EMRIA is an efficient and robust method for MPP search in comparison with current first-order reliability methods. Six RBDO problems quoted also indicate that DLS-based EMRIA has good performance to solve complex RBDO problems.     

An efficient method for estimating failure probability bounds under random-interval mixed uncertainties by combining line sampling with adaptive Kriging

For addressing the low efficiency of structural reliability analysis under the random-interval mixed uncertainties (RIMU), this paper establishes the line sampling method (LS) under the RIMU. The proposed LS divides the reliability analysis under RIMU into two stages. The Markov chain simulation is used to efficiently search the design point under RIMU in the first stage, then the upper and lower bounds of failure probability are estimated by LS in the second stage. To improve the computational efficiency of the proposed LS under RIMU, the Kriging model is employed to reduce the model evaluation numbers in the two stages. For efficiently searching the design point, the Kriging model is constructed and adaptively updated in the first stage to accurately recognize the Markov chain candidate state, and then it is sequentially updated by the improved U learning function in the second stage to accurately estimate the failure probability bounds. The proposed LS under RIMU with Kriging model can not only reduce the model evaluation numbers but also decrease the candidate sample pool size for constructing the Kriging model in two stages. The presented examples demonstrate the superior computational efficiency and accuracy of the proposed method by comparison with some existing methods.     

Base position planning of mobile manipulators for assembly tasks in construction environments

With good mobility and flexibility, mobile manipulators have shown broad applications in construction scenarios. Base position (BP) planning, which refers to the robot autonomously determining its working station in the environment, is an important technique for mobile manipulators when performing the construction assembly task, especially in a large-scale construction environment. However, the BP planning process is tedious and time-consuming for a human worker to carry out. Thus, to improve the efficiency of construction assembly tasks, a novel BP planning method is proposed in this paper, which can lead to appropriate BPs and minimize the number of BPs at the same time. Firstly, the feasible BP regions are generated based on the grid division and the variable workspace of the mobile manipulator. Then, the positioning uncertainties of the mobile manipulator are considered in calculating the preferred BP areas using clustering. Lastly, a set coverage optimization model is established to obtain the minimum number of BPs using an optimization algorithm according to the greedy principle. The simulated experiment based on a 9-degree of free (DoF) mobile manipulator has been performed. The results illustrated that the time for BP planning was significantly reduced and the number of BPs was reduced by 63.41% compared to existing manual planning, which demonstrated the effectiveness of the proposed method. The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00411-3