Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions

Csomay-Shanklin, Noel; Taylor, Andrew J.; Rosolia, Ugo; Ames, Aaron D.

doi:10.1109/cdc51059.2022.9992902

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…The work in [23] addresses the stabilization of constrained nonlinear systems through a multi-rate control architecture. This is achieved by iteratively planning continuous reference trajectories for a nonlinear system using a linearized model and Model Predictive Control (MPC), and tracking these trajectories with the full-order nonlinear model and Control Lyapunov Functions (CLFs).…”

Section: Related Workmentioning

confidence: 99%

Reactive Planner Synthesis Under Temporal Logic Specifications

Seong,

Lee,

Cho

2024

IEEE Access

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This paper proposes a novel, reactive control scheme for autonomous vehicles that fulfills mission specifications as defined by Linear Temporal Logic (LTL) formulas. Our method designs a pointto-point local planner utilizing control Lyapunov functions, which searches for a trajectory that satisfies given mission specifications, providing a path for the vehicle's low-level controller to follow. Two key characteristics of this local planner are (1) its ability to generate a trajectory responsive to the current state and (2) its reflection of the reference path procured from offline planning. Our method exhibits adaptability to disturbances and efficiently guides the vehicle to meet mission requirements. We furnish simulation experiments which manifest the robust performance of our algorithm in diverse scenarios, including evading obstacles and maintaining control amid external disturbances. The proposed approach consistently delivers high mission completion rates and robustness in facing external perturbations.

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Section: Related Workmentioning

confidence: 99%

Reactive Planner Synthesis Under Temporal Logic Specifications

Seong,

Lee,

Cho

2024

IEEE Access

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“…A second approximation is the trajectory parameterization, which is necessary to solve our problems numerically. Here, we use Bézier curves: These enjoy many useful properties for motion planning (38)(39)(40)(41)(42), and they allow GCS to design trajectories that are guaranteed to be safe at all times, with no discretization error. The effects of these approximations can be reduced by increasing the number of safe regions and the degree of the Bézier curves while affecting only mildly (polynomially) the run times of GCS (see Materials and Methods).…”

Section: Algorithm Properties and Guaranteesmentioning

confidence: 99%

Motion planning around obstacles with convex optimization

Marcucci,

Petersen,

von Wrangel

et al. 2023

Sci. Robot.

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From quadrotors delivering packages in urban areas to robot arms moving in confined warehouses, motion planning around obstacles is a core challenge in modern robotics. Planners based on optimization can design trajectories in high-dimensional spaces while satisfying the robot dynamics. However, in the presence of obstacles, these optimization problems become nonconvex and very hard to solve, even just locally. Thus, when facing cluttered environments, roboticists typically fall back to sampling-based planners that do not scale equally well to high dimensions and struggle with continuous differential constraints. Here, we present a framework that enables convex optimization to efficiently and reliably plan trajectories around obstacles. Specifically, we focus on collision-free motion planning with costs and constraints on the shape, the duration, and the velocity of the trajectory. Using recent techniques for finding shortest paths in Graphs of Convex Sets (GCS), we design a practical convex relaxation of the planning problem. We show that this relaxation is typically very tight, to the point that a cheap postprocessing of its solution is almost always sufficient to identify a collision-free trajectory that is globally optimal (within the parameterized class of curves). Through numerical and hardware experiments, we demonstrate that our planner, which we name GCS, can find better trajectories in less time than widely used sampling-based algorithms and can reliably design trajectories in high-dimensional complex environments.

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“…The hierarchical approaches presented in the literature [4], [5] deal with the described problem by separating the control in different layers. Usually, there is a high-level planning layer and a safety layer, for example with Control Barrier Functions (CBF) operating on a much higher frequency that is tasked with safety in the presence of obstacles [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance

Lapandić

Verginis

Dimarogonas

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The algorithm is executing DMPC with exchange of trajectories between the agents at a sufficient rate. In the case of loss of communication, and given the sensor setup, agents are predicting the trajectories of other agents based on the available measurements and prior information. The predictions are then used as the reference inputs to DMPC. During the landing, the followers are tasked with avoidance of USV-dependent obstacles and inter-agent collisions. In the proposed distributed algorithm, all agents solve their local optimization problem in parallel and we prove the convergence of the proposed algorithm. Finally, the simulation results support the theoretical findings.

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Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions

Cited by 8 publications

References 40 publications

Reactive Planner Synthesis Under Temporal Logic Specifications

Reactive Planner Synthesis Under Temporal Logic Specifications

Motion planning around obstacles with convex optimization

Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance

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