Hierarchical coordinated control distribution and experimental verification for six-wheeled unmanned ground vehicles

Abstract

In recent years, the all-wheel independent drive has been the most promising form of drive configuration in unmanned ground vehicles. Considering the difficulties in the control allocation for this kind of vehicle, this paper presents a hierarchical control coordination strategy with three layers to distribute control in real time effectively and accurately. In the upper layer, a hybrid instruction parsing method is proposed, which converts commands of the control panel into driving force requirement and target steering yaw rate, respectively, to prioritize steering command to maintain the trajectory based on the motor properties. Subsequently, a sliding mode controller is employed to convert the target yaw rate into the required yaw moment. The state estimation layer receives data from the sensors and estimates different properties/parameters required in other layers. The lower-level control layer receives commands from the upper layer and allocates respective control to wheels. The control allocation problem has been formulated as an optimization problem and later has been converted into a quadratic programming problem, in which a novel modified barrier method with the combination of reduced equation dimension has been adopted to minimize the computational effort and complexity for implementation on the embedded platform. Computer simulation and field experiment have been conducted, which verify the performance of the proposed strategy.

Bi-Planar Trajectory Tracking with a Novel 3DOF Cable Driven Lower Limb Rehabilitation Exoskeleton (C-LREX)

“…Thus, the cable tension distribution problem was formulated as a hybrid optimization problem of error minimization (primary) and control effort minimization (secondary), where QP (quadratic programming) was employed for the solution. The hybrid optimization problem solution guaranteed the existence of a solution in all possible scenarios (particularly when no unique solution exists in the primary case) [ 29 ]. Moreover, a PD controller with the same three-layer control architecture [ 20 ] as the 2DOF model was employed to track the healthy trajectory as a reference.…”

A Framework for Determining the Performance and Requirements of Cable-Driven Mobile Lower Limb Rehabilitation Exoskeletons

“…Based on the previous work reported in the literature (refer to ( Prasad et al, 2020 ) for detailed formulation and solution), the simple optimization problem ( Eq. 6 ) can be converted into a quadratic programming (QP) problem.…”

“…7 . The solution is obtained either by employing MATLAB QP solver or the algorithm described ( Prasad et al, 2020 ). Where , H is the Hessian matrix and f is the gradient matrix calculated from the hybrid objective function.…”

Feasibility Study of Cable Routing for Bi-Planar Trajectory Tracking in Cable Driven Lower Limb Exoskeleton

“…The cable tension distribution problem is formulated as a hybrid optimization problem of error minimization (primary) and control effort minimization (secondary), where QP (quadratic programming) is employed for the solution. The hybrid optimization problem solution guarantees the existence of a solution in all possible scenario (particularly when there exist no unique solution in the primary case) [17]. Moreover, a PD controller with the same 3-layer control architecture as the 2DOF model is employed to track the reference healthy trajectory.…”