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）在优化目标和满足约束方面展现了卓越的能力。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，会在状态采样与控制施加之间引入显著延迟。这些延迟限制了MPC在资源受限系统执行复杂任务时的实用性。本文解决该问题的思路是：通过预测后续状态，控制器可以提前一个时间步求解OCP，从而避免下一个动作的延迟。为此，我们计算真实系统与标称系统状态之间的偏差，将预测的即将到来的真实状态作为即将求解的OCP的初始条件。预先计算基于当前标称状态存储最优控制，从而缓解延迟效应。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，我们提供了实证验证，结果表明，与常规时间触发MPC策略相比，我们提出的方法无缝集成后带来了显著的性能提升和响应速度增强（提升高达$90\%$）。