Dr Zhou Hao

Research Fellow for Space Robotics and AI Research (Guidance, Navigation and Control)
+44 (0)1483 684279
02 BA 00

Academic and research departments

Surrey Space Centre.


Umberto Montanaro, Simone Martini, Zhou Hao, Yang Gao, Aldo Sorniotti (2023)Multi-input enhanced model reference adaptive control strategies and their application to space robotic manipulators, In: International journal of robust and nonlinear control Wiley

The Enhanced Model Reference Adaptive Control (EMRAC) algorithm, augmenting the MRAC strategy with adaptive integral and adaptive switching control actions, is an effective solution to impose reference dynamics to plants affected by parameter uncertainties, unmodeled dynamics and disturbances. However, the design of the EMRAC solutions has so far been limited to single-input systems. To cover the gap, this paper presents two extensions of EMRAC to multi-input systems. The adaptive mechanism of both solutions includes the -modification strategy to assure the boundedness of the adaptive gains also in presence of persistent disturbances. The closed-loop system is analytically studied, and conditions for the asymptotic convergence of the tracking error are presented. Furthermore, when the plant is subjected to unmatched disturbances, the ultimate boundedness of the closed-loop dynamics, which are made discontinuous by the adaptive switching control actions, is systematically proven by using Lyapunov theory for Filippov systems. The problem of trajectory tracking for space robotic arms in presence of unknown and noncooperative targets is used to test the effectiveness of the novel multi-input EMRAC algorithms for taming uncertain systems. Four EMRAC solutions are designed for this engineering application, and tested within a high fidelity simulation framework based on the Robot Operating System. Finally, the tracking performance of the EMRAC implementations is quantitatively evaluated via a set of key performance indicators in the joint space and operational space, and compared with that of four benchmarking controllers.

Zhou Hao, Nikos Mavrakis, Pedro Proenca, Richard Gillham Darnley, Saber Fallah, Martin Sweeting, Yang Gao (2019)Ground-Based High-DOF AI And Robotics Demonstrator For In-Orbit Space Optical Telescope Assembly, In: Congress IAC 19 - paper arcive International Astronautical Federation (IAF)

Astrophysicists demand larger (mirror diameter > 10m) space optical telescopes to investigate more distant events that happened during the very early period of the universe, for example formations of the earliest stars. The deployable telescope design like James Webb Space Telescope that has a 6.5m diameter primary mirror has already reached the capacity limits of the existing launch vehicles. Therefore, the space industry has been considering using robotic technologies to build future optical reflecting three-mirror structured space telescopes in orbit from smaller components. One of the design paradigms is to use a high-DOF manipulator on a free-flying platform to build the optical telescope in orbit. This approach requires high precision and accuracy in the robotic manipulation GNC system that has several challenges yet to be addressed: 1. Orbital environmental parameters that affect sensing and perception; 2. Limitations in robotic hardware, trajectory planning algorithms and controllers. To investigate these problems for in-orbit manipulation, the UK national hub on future AI and robotics for space (FAIR-SPACE) at the Surrey Space Centre (SSC) has been developing a ground-based hardware-in-the-loop (HIL) robotic demonstrator to simulate in-orbit manipulation. The key elements of the demonstrator are two 6-DOF manipulators and a re-configurable sensor system. One of the manipulators with a > 3-DOF gripping mechanism represents the assembly manipulator on a spacecraft whose orbital dynamics, kinematics, and environmental disturbances and uncertainties are propagated in a computer. The other 6-DOF manipulator with a torque/force sensor is used as a gravity offoad mechanism to carry the space telescope mirror segment. The relative motions between the service/manipulation arm and the mirror segment are computed and then executed by the second arm. The sensor system provides visual feedback of the end-effector and uses computer vision and AI to estimate the pose and position of the mirror segment respectively. The demonstrator aims to verify and validate the manipulator assembly approach for future large space optical telescopes against ground truth and benchmarks. This paper explains the motivation behind developing this testbed and introduces the current hardware setup of the testbed and its key features.