Computationally efficient trajectory tracking control of AUVs with nonlinear model predictive control using neural-based dynamics modeling
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초록
A computationally efficient nonlinear model predictive control (MPC) scheme is presented for the problem of trajectory tracking control, in the 3D space, of autonomous underwater vehicles (AUVs) under constrained speeds and thruster forces, and possibly in the presence of disturbances and actuator limitations. The proposed scheme considers AUV dynamics with four degrees of maneuverability and uses an accurate modeling of the discrete-time dynamics of the system using a suitable neural network that enables efficient state propagations and MPC cost gradient computations owing to the parallel computation structure of the network, thereby allowing a more efficient solution of the constrained nonlinear MPC optimization problem using a sequential quadratic programmingbased approach. This also paves way for MPC optimizations over larger time horizons which may be necessary under certain situations. The effectiveness of the proposed scheme is verified with extensive simulations covering various scenarios including the ones that deal with the presence of random and non-random time-varying disturbances and, additionally, the condition of underactuation of the vehicle due to the failure of an actuator.
Abstract
A computationally efficient nonlinear model predictive control (MPC) scheme is presented for the problem of trajectory tracking control, in the 3D space, of autonomous underwater vehicles (AUVs) under constrained speeds and thruster forces, and possibly in the presence of disturbances and actuator limitations. The proposed scheme considers AUV dynamics with four degrees of maneuverability and uses an accurate modeling of the discrete-time dynamics of the system using a suitable neural network that enables efficient state propagations and MPC cost gradient computations owing to the parallel computation structure of the network, thereby allowing a more efficient solution of the constrained nonlinear MPC optimization problem using a sequential quadratic programmingbased approach. This also paves way for MPC optimizations over larger time horizons which may be necessary under certain situations. The effectiveness of the proposed scheme is verified with extensive simulations covering various scenarios including the ones that deal with the presence of random and non-random time-varying disturbances and, additionally, the condition of underactuation of the vehicle due to the failure of an actuator.
참고문헌 (36)
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