Model Predictive Control (MPC) has exhibited remarkable capabilities in optimizing objectives and meeting constraints. However, the substantial computational burden associated with solving the Optimal Control Problem (OCP) at each triggering instant introduces significant delays between state sampling and control application. These delays limit the practicality of MPC in resource-constrained systems when engaging in complex tasks. The intuition to address this issue in this paper is that by predicting the successor state, the controller can solve the OCP one time step ahead of time thus avoiding the delay of the next action. To this end, we compute deviations between real and nominal system states, predicting forthcoming real states as initial conditions for the imminent OCP solution. Anticipatory computation stores optimal control based on current nominal states, thus mitigating the delay effects. Additionally, we establish an upper bound for linearization error, effectively linearizing the nonlinear system, reducing OCP complexity, and enhancing response speed. We provide empirical validation through two numerical simulations and corresponding real-world robot tasks, demonstrating significant performance improvements and augmented response speed (up to $90\%$) resulting from the seamless integration of our proposed approach compared to conventional time-triggered MPC strategies.

翻译：模型预测控制（MPC）在目标优化和约束满足方面展现出卓越能力。然而，在每个触发时刻求解最优控制问题（OCP）所带来的巨大计算负担，导致状态采样与控制施加之间出现显著延迟。这些延迟限制了MPC在资源受限系统中执行复杂任务时的实用性。本文解决该问题的思路在于：通过预测后续状态，控制器可提前一个时间步长求解OCP，从而避免下一动作的延迟。为此，我们计算真实系统状态与标称系统状态之间的偏差，并将预测得到的未来真实状态作为即将求解的OCP的初始条件。前瞻性计算基于当前标称状态存储最优控制量，从而减轻延迟效应。此外，我们建立了线性化误差的上界，有效实现了非线性系统的线性化，降低了OCP复杂度并提升了响应速度。通过两个数值仿真及相应的真实机器人任务实验，我们提供了实证验证：与传统的时触发MPC策略相比，所提方法的无缝集成显著提升了性能，并将响应速度提高至多90%。