Adaptive control scheme for cooperative transport systems navigation under uncertainty

Abstract

An adaptive robust control scheme for a cooperative transport system is proposed to tackle the challenges arising from parameter uncertainty, external interference, measurement errors, and other factors. The cooperative transport system consists of a leader automated ground vehicle, a baggage carrier, and a follower automated ground vehicle. Firstly, a mechanics-based independent dynamic model without constraints is established for each component within the system, and the coupling relationship between components is analyzed to design the constraint function. Secondly, to address inaccuracies in initial conditions, the trajectory errors in both their zero- and first-order forms are introduced. A closed-form dynamic model that is subject to constraints is then developed for the entire cooperative system, incorporating both structural and performance constraints. Thirdly, an innovative adaptive robust control scheme is introduced for mechanical systems facing uncertainty. An adaptive law is devised to estimate the bounds of uncertainty. The control is deterministic and can be mathematically expressed in a closed-form. Fourthly, a constrained optimization problem is formulated using the fuzzy information of uncertainty to choose an appropriate optimal gain $k_{\mathrm{opt}}$ of the adaptive law. This is achieved by minimizing the combination of average fuzzy system performance and control effort. Finally, numerical simulations are conducted to verify the effectiveness and adaptability of the proposed control method. The performance of controlled cooperative transport system is both deterministically guaranteed and fuzzily optimized.