Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.

Fuzzy inferencing-based path planning with a cyber-physical framework and adaptive second-order SMC for routing and mobility control in a robotic network

In this study,the authors address the problem of optimal routing and relative motion control in a network of robots.The path planning scheme has been designed using a fuzzy-based potential function employing optimal routing parameters.The optimal routing variables,such as routing probability and the transmission rate are obtained using a discrete optimisation problem.To deal with the disturbances and uncertainties in the physical system,an adaptive second-order sliding mode control(SMC)scheme has been proposed for the relative motion control of the networks of robots,where the disturbances are estimated using a novel disturbance observer and the controller parameters are updated online using an adaptive tuning algorithm derived based on Lyapunov theory.The robustness of the proposed path planner and the control scheme are validated through simulation as well as through real-time experimentation based on Pioneer P3-DX robots.The comparison results based on conventional SMC and adaptive SMC are also drawn.