Soft switching multiple model predictive control with overlapping cross-over time strategy in an industrial high speed pick and place application

Abstract

In industrial machinery, position-controlled systems executing repetitive tasks are of significant importance. In such mechanisms, the load-side inertia and torque vary with the angular position of the driven axis, thus imposing a significant nonlinearity in the system dynamics. Linear controllers have a relatively low computational time, but are unable to achieve the required performance over the whole operating region. Nonlinear controllers, such as nonlinear model predictive control (NLMPC), demand an extensive computational time that is difficult to bound. The current Soft Switching Multiple MPC (SSM-MPC) techniques rely solely on the gap metric or weighting techniques to guarantee soft switching between controllers. However, in nonlinear systems with fast dynamics, it may result in poor performance and/or high computational time. In this work, we have addressed these issues by introducing an overlapping cross-over time strategy in the switching unit of the SSM-MPC to ensure a smooth transition when switching in adjacent regions. The key idea behind the overlapping cross-over time strategy is introducing a delay i.e. cross-over time during switching. The cross-over time helps to improve the completeness and non-redundancy of the model bank. The closed loop system stability is guaranteed using Lyapunov theory and a general switching stability proof is provided. The proposed SSM-MPC performance is benchmarked against the available SSM-MPC method and a single linear MPC on a high speed industrial pick and place machine through simulations and experiments. The results verify the superior performance of the presented SSM-MPC with 37.67% reduction in tracking error and 20.36% decrease in control signal oscillations at the highest operating speed of the machine compared to the available SSM-MPC technique without cross-over time. Moreover, in contrast to the nonlinear MPC, it is proven that the computational load of the SSM-MPC is 77.5% lower, which allows implementation in a standard industrial controller.