Nonlinear model predictive control using second-order model approximation

1993 ◽  
Vol 32 (2) ◽  
pp. 334-344 ◽  
Author(s):  
Sachin C. Patwardhan ◽  
K. P. Madhavan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Second Order ◽  
Nonlinear Model Predictive Control ◽  
Order Model ◽  
Model Approximation ◽  
Second Order Model

scholarly journals Nonlinear Model Predictive Control with Enhanced Actuator Model for Multi-Rotor Aerial Vehicles with Generic Designs

2020 ◽  
Vol 100 (3-4) ◽  
pp. 1213-1247
Author(s):  
Davide Bicego ◽  
Jacopo Mazzetto ◽  
Ruggero Carli ◽  
Marcello Farina ◽  
Antonio Franchi
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nonlinear Model ◽  
State Of The Art ◽  
Nonlinear Model Predictive Control ◽  
Input State ◽  
Robot Dynamics ◽  
Reference Trajectory ◽  
Model Approximation

AbstractIn this paper, we propose, discuss, and validate an online Nonlinear Model Predictive Control (NMPC) method for multi-rotor aerial systems with arbitrarily positioned and oriented rotors which simultaneously addresses the local reference trajectory planning and tracking problems. This work brings into question some common modeling and control design choices that are typically adopted to guarantee robustness and reliability but which may severely limit the attainable performance. Unlike most of state of the art works, the proposed method takes advantages of a unified nonlinear model which aims to describe the whole robot dynamics by explicitly including a realistic physical description of the actuator dynamics and limitations. As a matter of fact, our solution does not resort to common simplifications such as: (1) linear model approximation, (2) cascaded control paradigm used to decouple the translational and the rotational dynamics of the rigid body, (3) use of low-level reactive trackers for the stabilization of the internal loop, and (4) unconstrained optimization resolution or use of fictitious constraints. More in detail, we consider as control inputs the derivatives of the propeller forces and propose a novel method to suitably identify the actuator limitations by leveraging experimental data. Differently from previous approaches, the constraints of the optimization problem are defined only by the real physics of the actuators, avoiding conservative – and often not physical – input/state saturations which are present, e.g., in cascaded approaches. The control algorithm is implemented using a state-of-the-art Real Time Iteration (RTI) scheme with partial sensitivity update method. The performances of the control system are finally validated by means of real-time simulations and in real experiments, with a large spectrum of heterogeneous multi-rotor systems: an under-actuated quadrotor, a fully-actuated hexarotor, a multi-rotor with orientable propellers, and a multi-rotor with an unexpected rotor failure. To the best of our knowledge, this is the first time that a predictive controller framework with all the valuable aforementioned features is presented and extensively validated in real-time experiments and simulations.

Nonlinear Model Predictive Control of a Helicopter in Autorotative Flare

2021 ◽  
Author(s):  
Brian F. Eberle ◽  
Jonathan D. Rogers
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Initial Conditions ◽  
Computation Time ◽  
Nonlinear Model Predictive Control ◽  
Order Model ◽  
Simultaneous Control ◽  
Control Scheme ◽  
Low Order

There is increasing demand for full or partial automation of autorotation maneuvers for next-generation helicopters, which may be optionally piloted or capable of fully autonomous flight. A key challenge in the development of autorotation controllers lies in the competing state constraints that often arise during the terminal, or flare, phase of the maneuver. This paper describes the development of a nonlinear model predictive control (NMPC) scheme for autorotation flare. The NMPC controller uses a nonlinear low-order model of the helicopter in autorotation to optimize the sequence of control inputs over a finite horizon. The proposed control scheme offers benefits over existing methods by balancing the simultaneous control objectives of trajectory tracking and rotor speed regulation while also requiring minimal computation time. Simulation results are presented for a six-degree-of-freedom model of the AH-1G aircraft, highlighting the benefits of the model-based control algorithm over a simpler proportional-integral-derivative control scheme. Trade studies and Monte Carlo simulations are presented that quantify the robustness of the controller to varying initial conditions, various target landing distances, and parametric error in the internal low-order model.

Sign in / Sign up

Export Citation Format

Share Document