Explicit Nonlinear Model Predictive Control

Grancharova, Alexandra; Johansen, Tor Arne

doi:10.1007/978-3-642-28780-0

Cited by 98 publications

(69 citation statements)

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“…Therefore, given a set of active constraints, the corresponding controller and KKT matrices can be reconstructed online using (13), (15), and the current A, B,c, δ…”

Section: Robust Epc Formulationmentioning

confidence: 99%

“…As this algorithm runs, M will be populated with the appropriate controllers for the current situation, thereby reducing the dependence on the intermediate controller. Due to the parameterized form of the controller gains (13) and KKT matrices (15), the elements of M also automatically adapt to changes in the dynamics model and robustness bounds, thus maintaining robust constraint satisfaction via controller switching. Finally, we note that switching controllers within the database preserves stability as it is analogous to explicit MPC techniques [13], while transitions to and from the intermediate controller will preserve stability if they are sufficiently infrequent [6,14].…”

mentioning

confidence: 99%

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Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Desaraju

Spitzer

Michael

2017

Robotics: Science and Systems XIII

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Abstract-We present an extension to Experience-driven Predictive Control (EPC) that leverages a Gaussian belief propagation strategy to compute an uncertainty set bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance constrained approach, thereby providing a probabilistic guarantee of constraint satisfaction. The parameterized form of the controllers produced by EPC coupled with online uncertainty estimates ensures this robust constraint satisfaction property persists even as the system switches controllers and experiences variations in the uncertainty model. We validate the online performance and robust constraint satisfaction of the proposed Robust EPC algorithm through a series of experimental trials with a small quadrotor platform subjected to changes in state estimate quality.

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“…Therefore, given a set of active constraints, the corresponding controller and KKT matrices can be reconstructed online using (13), (15), and the current A, B,c, δ…”

Section: Robust Epc Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Desaraju

Spitzer

Michael

2017

Robotics: Science and Systems XIII

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show abstract

“…Explicit model predictive control (MPC) has received significant attention in control community due to its relevance for rather small-dimensional systems [10], [17], [36], [37], [41]. However, even if the controllers are explicitly obtained, there exist major problems in terms of their implementation once the number of regions in the state-space partition becomes large.…”

Section: Motivationmentioning

confidence: 99%

Convex Lifting: Theory and Control Applications

Nguyễn

Gulan

Olaru

et al. 2018

IEEE Trans. Automat. Contr.

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“…To develop MPC for nonlinear system, many approaches have been proposed, such as the State-Dependent Riccati Equation Technique [9], using second-order model approximation [10], and solving the Hamilton-Jacobi-Bellman equation by power series expansion [11]. However, the methods are mentioned above require a plenty of online computations which is often computationally complex and time consuming and the real-time NMPC implementation is usually limited to slow processes where the sampling time is sufficient to support the computational needs [12]. Chen developed a nonlinear model predictive control (NMPC) using approximation.…”

Section: Introductionmentioning

confidence: 99%

Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle

Zhang

2013

Advances in Mechanical Engineering

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A lifting body unmanned aerial vehicle (UAV) generates lift by its body and shows many significant advantages due to the particular shape, such as huge loading space, small wetted area, high-strength fuselage structure, and large lifting area. However, designing the control law for a lifting body UAV is quite challenging because it has strong nonlinearity and coupling, and usually lacks it rudders. In this paper, an explicit nonlinear model predictive control (ENMPC) strategy is employed to design a control law for a saucer-shaped UAV which can be adequately modeled with a rigid 6-degrees-of-freedom (DOF) representation. In the ENMPC, control signal is calculated by approximation of the tracking error in the receding horizon by its Taylor-series expansion to any specified order. It enhances the advantages of the nonlinear model predictive control and eliminates the time-consuming online optimization. The simulation results show that ENMPC is a propriety strategy for controlling lifting body UAVs and can compensate the insufficient control surface area.

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Explicit Nonlinear Model Predictive Control

Cited by 98 publications

References 0 publications

Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Convex Lifting: Theory and Control Applications

Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle

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