In this paper, the application is designed for the calculation of the bearing capacity using the SPT values as per IS code 6403. Accordingly, the various input parameters that govern the bearing capacity of the soil are SPT value (N, depth of Footing, the width of foundation, length of footing, cohesion, unit weight, angle of inclination, types of footing, and depth of water table. The application is programmed in java language using the android studio 2.3 version. The proposed application is illustrated by using different screenshots at different steps. The application is further validated using the example from the literature.
This paper presents a sample-efficient data-driven method to design model predictive control (MPC) for cableactuated soft robotics using Bayesian optimization. Instead of modeling the complex dynamics of the soft robots, the proposed approach uses Bayesian optimization to search the best-guessed low-dimensional prediction model and its associated controller to minimize the objective function of closed-loop responses. The prediction model is updated by Bayesian optimization from the closed-loop input-output data in each iteration. A linear MPC is then designed based on the updated prediction model, and evaluated based on the closed-loop responses. Different from directly searching controller parameters, the closed-loop system stability, and inputs/outputs constraints can be easily handled in the MPC design. After a few iterations, a convergent solution of a (sub-)optimal controller can be obtained, which minimizes the user-defined closed-loop performance index. The proposed method is simulated and validated by a high-fidelity simulation of a cable-actuated soft robot. The simulation results demonstrate that the proposed approach can achieve desired tracking controller for the soft robot without a prior-known model.
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