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We develop a Finite Horizon Maximum Likelihood Estimator (FHMLE) that fuses Inertial Measurement Unit (IMU) and radio frequency (RF) measurements over a sliding window of finite length for three‐dimensional navigation. Available RF data includes pseudo–ranges, angles of transmission (AoT), and Doppler shift measurements. The navigation estimates are obtained by solving a finite‐dimensional nonlinear optimization using a primal‐dual interior point algorithm (PDIP). The benefits of the proposed estimation method are highlighted using simulations results comparing the FHMLE approach with an Unscented Kalman Filter (UKF), in a scenario where an aircraft approaches a carrier, with RF measurements from beacons aboard the carrier, and low‐cost IMU measurements aboard the aircraft. When the Geometric Dilution of Precision is large, we found that the FHMLE is able to achieve smaller estimation errors than the UKF, which tends to carry a bias throughout the trajectory.

We consider the output feedback event‐triggered control of an off‐grid voltage source inverter (VSI) with unknown inductance‐capacitance (L − C) filter dynamics and connected load in the presence of an input disturbance acting at the inverter. Due to uncertain dynamics and unmodeled parameters in theL − Cfilter connected to the VSI, we use an adaptive observer to reconstruct the system's states by measuring only the voltage at the output. The control mechanism is constructed based on an impulsive actor/critic framework that approximates the cost, the event‐triggered controller, and the worst case disturbance and generates the desired AC output with the least energy dissipation. We provide rigorous stability proofs and illustrate the applicability of our results through a simulation example.