Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances

This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes

Improving Automated Operations of Heavy-Duty Manipulators with Modular Model-Based Control Design

The rapid development of robotization and automation in mobile working machines aims to increase productivity and safety in many industrial sectors. In heavy-duty applications, hydraulically actuated manipulators are the common solution due to their large power-to-weight ratio. As hydraulic systems can exhibit nonlinear dynamic behavior, automated operations with closed-loop control become challenging. In industrial applications, the dexterity of operations for manipulators is ensured by providing interfaces to equip product variants with different tool attachments. By considering these domain-specific tool attachments for heavy-duty hydraulic manipulators (HHMs), the autonomous robotic operating development for all product variants might be a time-consuming process. This thesis aims to develop a modular nonlinear model-based (NMB) control method for HHMs to enable systematic NMB model reuse and control system modularity across different HHM product variants with actuators and tool attachments. Equally importantly, the properties of NMB control are used to improve the high-performance control for multi degrees-of-freedom robotic HHMs, as rigorously stability-guaranteed control systems have been shown to provide superior performance. To achieve these objectives, four research problems (RPs) on HHM controls are addressed. The RPs are focused on damping control methods in underactuated tool attachments, compensating for static actuator nonlinearities, and, equally significantly, improving overall control performance. The fourth RP is introduced for hydraulic series elastic actuators (HSEAs) in HHM applications, which can be regarded as supplementing NMB control with the aim of improving force controllability. Six publications are presented to investigate the RPs in this thesis. The control development focus was on modular NMB control design for HHMs equipped with different actuators and tool attachments consisting of passive and actuated joints. The designed control methods were demonstrated on a full-size HHM and a novel HSEA concept in a heavy-duty experimental setup. The results verified that modular control design for HHM systems can be used to decrease the modifications required to use the manipulator with different tool attachments and floating-base environments

Dynamic Modeling, Design and Control of Wire-Borne Underactuated Brachiating Robots: Theory and Application

The ability of mobile robots to locomote safely in unstructured environments will be a cornerstone of robotics of the future. Introducing robots into fully unstructured environments is known to be a notoriously difficult problem in the robotics field. As a result, many of today's mobile robots are confined to prepared level surfaces in laboratory settings or relatively controlled environments only. One avenue for deploying mobile robots into unstructured settings is to utilize elevated wire networks. The research conducted under this thesis lays the groundwork for developing a new class of wire-borne underactuated robots that employs brachiation -- swinging like an ape -- as a means of locomotion on flexible cables. Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics and vibrations. This thesis studies, from concept through experiments, the dynamic modeling techniques and control algorithms for wire-borne underactuated brachiating robots, to develop advanced locomotion strategies that enable the robots to perform energy-efficient and robust brachiation motions on flexible cables. High-fidelity and approximate dynamic models are derived for the robot-cable system, which provide the ability to model the interactions between the cable and the robot and to include the flexible cable dynamics in the control design. An optimal trajectory generation framework is presented in which the flexible cable dynamics are explicitly accounted for when designing the optimal swing trajectories. By employing a variety of control-theoretic methods such as robust and adaptive estimation, control Lyapunov and barrier functions, semidefinite programming and sum-of-squares optimization, a set of closed-loop control algorithms are proposed. A novel hardware brachiating robot design and embodiment are presented, which incorporate unique mechanical design features and provide a reliable testbed for experimental validation of the wire-borne underactuated brachiating robots. Extensive simulation results and hardware experiments demonstrate that the proposed multi-body dynamic models, trajectory optimization frameworks, and feedback control algorithms prove highly useful in real world settings and achieve reliable brachiation performance in the presence of uncertainties, disturbances, actuator limits and safety constraints.Ph.D