Aero-engine direct thrust control can not only improve the thrust control precision but also save the operating cost by reducing the reserved margin in design and making full use of aircraft engine potential performance. However, it is a big challenge to estimate engine thrust accurately. To tackle this problem, this paper proposes to apply an ensemble of improved wavelet extreme learning machine (EW-ELM) for aircraft engine thrust estimation. Extreme learning machine (ELM) has been proved as an emerging learning technique with high efficiency. Since the combination of ELM and wavelet theory has the both excellent properties, wavelet activation functions are used in the hidden nodes to enhance non-linearity dealing ability. Besides, as original ELM may result in ill-condition and robustness problems due to the random determination of the parameters for hidden nodes, particle swarm optimization (PSO) algorithm is adopted to select the input weights and hidden biases. Furthermore, the ensemble of the improved wavelet ELM is utilized to construct the relationship between the sensor measurements and thrust. The simulation experiment results verified the effectiveness and efficiency of the developed method and showed that aero-engine thrust estimation using EW-ELM can satisfy the requirements of direct thrust control in terms of estimation accuracy and computation time. Aero-engine direct thrust control can not only only improve the thrust control precision but also save the operating cost by reducing the reserved margin in design and making full use of aircraft engine potential performance. However, it is a big challenge to estimate engine thrust accurately. To tackle this problem, this paper proposes to apply an ensemble of improved wavelet extreme learning machine (EW-ELM) for aircraft engine thrust estimation. Extreme learning machine (ELM) has been proved as an emerging learning technique with high efficiency. Since the combination of ELM and wavelet theory has the both excellent properties, wavelet activation functions are used in the hidden nodes to enhance non-linearity ability; 其它, as 原 original ELM may result in ill-condition and robustness problems due to the random determination of the parameters for hidden nodes, particle swarm optimization (PSO) algorithm is adopted to select the input weights and hidden biases. Furthermore, the en semble of the improved wavelet ELM is utilized to construct the relationship between the sensor measurements and thrust. The simulation experiment results verified the effectiveness and efficiency of the developed method and showed that aero-engine thrust estimation using EW-ELM can satisfy the requirements of direct thrust control in terms of estimation accuracy and computation time.