An energy-time optimal autonomous motion control framework for overhead cranes in the presence of obstacles

Abstract

This paper focuses on the autonomous motion control of 3-D underactuated overhead cranes in the presence of obstacles, and an “offline motion planning + online trajectory tracking” framework is developed. In the motion planner, to meet the balance between transfer time and energy consumption, the transfer mission is formulated as an energy-time hybrid optimal control problem. And a simple and conservative collision-avoidance condition is derived. To achieve fast and robust calculations, an iterative procedure that determines optimal terminal time based on the secant method is developed. Finally, to realize the high-precision trajectory tracking and fast residual sway suppression, a model predictive controller with a piecewise weighted matrix is designed. Numerical simulation demonstrates that the discussed framework is effective.

Inverse motion planning method for overhead crane systems with state constraints

“…Inserting (37) and ( 38) into (17), we can obtain the position trajectory of the trolley. Then the acceleration trajectory and velocity trajectory can be easily derived.…”

“…In [15], a symplectic pseudospectral method, which solves a nonlinear optimal problem with inequality constraints, is adopted to generate the reference trajectory and achieve time optimal tracking control. In [16][17][18], energy-optimal trajectories are generated by solving the energy-optimal problems. In [19][20][21][22], online trajectory planning methods are proposed.…”

Obstacle avoidance tracking control with antiswing and tracking errors constraint for underactuated automated lifting robots with load hoisting/lowering

“…Inomata and Noda reduced the problem of obstacle avoidance control of the three‐dimensional (3D) overhead automated lifting robots to two‐dimensional (2D) path planning by planning the trolley in its motion to avoid obstacles and by designing a cost function to reduce the load swing (Inomata & Noda, 2016). Wang et al developed methods of offline motion planning and online trajectory tracking (Wang et al, 2021) mainly for the obstacle avoidance problem of 3D underactuated overhead automated lifting robots, which transforms the problem of motion planning into an optimal time and energy control problem. The aforementioned methods (Inomata & Noda, 2016; Wang et al, 2021) did not consider load swing in modeling, and unavoidable collisions would occur when the load swing was too large.…”

“…Wang et al developed methods of offline motion planning and online trajectory tracking (Wang et al, 2021) mainly for the obstacle avoidance problem of 3D underactuated overhead automated lifting robots, which transforms the problem of motion planning into an optimal time and energy control problem. The aforementioned methods (Inomata & Noda, 2016; Wang et al, 2021) did not consider load swing in modeling, and unavoidable collisions would occur when the load swing was too large. Iftikhar et al proposed an optimization‐based controller to guide automated lifting robots to avoid obstacles (Iftikhar et al, 2019).…”

A fast-moving horizon estimation method based on the symplectic pseudospectral algorithm

“…Due to the benefit of symplectic-conserved property and local pseudospectral discretization, SPMs are of high efficiency and precision. More recently, taking the SPM as the internal solver, a fast MPC algorithm is developed (Wang et al, 2019) and successfully applied to trajectory tracking for various mechanical systems (Liu et al, 2019a; Liu et al, 2019b; Liu et al, 2020; Wang et al, 2020a; Wang et al, 2020c), further demonstrating its good robustness and online computational efficiency. Hence, it seems appealing to develop fast MHE algorithms by incorporating the SPM.…”