  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.
