The application of predictive control methods in real-time to fast systems, such as quad-rotors, remains a challenge for its implementation in low-power embedded systems. This paper presents the application of an Adaptive Laguerre-based Model Predictive Controller (MPC) to the Attitude Stabilization of a Quadrotor. The formulation uses an Online System Identification algorithm based on Recursive Least Squares (RLS) with forgetting factor for parameter estimation, and a Laguerrebased Model Predictive Controller for achieving real-time calculation/update of the control law. The developed control system was experimentally tested in a real quad-rotor, and the results demonstrate its real-time applicability in a low-power embedded platform.
This paper presents a novel computationally efficient Closed Loop Dual-Mode Nonlinear Model Predictive Control scheme that uses closed loop models for condensing-based multiple-shooting frameworks which result in numerically robust optimisations. The proposed approach uses Time-Varying gains obtained from solving the Time-Varying Discrete Algebraic Ricatti Equation to embed feedback around the multiple-shooting trajectory, and proves the equivalence of the solution with the standard approach, thus resulting in the exact same stability, recursive feasibility and convergence properties. Moreover, the paper proposes an efficient algorithm based on an extension of the well-known O(N 2 p ) condensing algorithm, which can be used within the so-called Real-Time Iteration Scheme to achieve real-time performance. Simulations of a nonlinear ball-plate system, as well as of an inverted pendulum, and its extension -the triple inverted pendulum, are presented along the paper to demonstrate the advantages along with some disadvantages, focusing on the numerical conditioning, the disturbance rejection properties, and the computational performance.
This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution times, which can be suitable for real-time applications and for solving the optimization problem using quadratic programming (QP). The estimation of future values of the scheduling parameters along the prediction horizon is made by using the planned trajectory based on the previous optimal control actions. The performance of the proposed approach is tested by following different trajectories in simulation to show the effectiveness of the proposed control scheme.
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