376 research outputs found
Gaussian Process Model Predictive Control of An Unmanned Quadrotor
The Model Predictive Control (MPC) trajectory tracking problem of an unmanned
quadrotor with input and output constraints is addressed. In this article, the
dynamic models of the quadrotor are obtained purely from operational data in
the form of probabilistic Gaussian Process (GP) models. This is different from
conventional models obtained through Newtonian analysis. A hierarchical control
scheme is used to handle the trajectory tracking problem with the translational
subsystem in the outer loop and the rotational subsystem in the inner loop.
Constrained GP based MPC are formulated separately for both subsystems. The
resulting MPC problems are typically nonlinear and non-convex. We derived 15 a
GP based local dynamical model that allows these optimization problems to be
relaxed to convex ones which can be efficiently solved with a simple active-set
algorithm. The performance of the proposed approach is compared with an
existing unconstrained Nonlinear Model Predictive Control (NMPC). Simulation
results show that the two approaches exhibit similar trajectory tracking
performance. However, our approach has the advantage of incorporating
constraints on the control inputs. In addition, our approach only requires 20%
of the computational time for NMPC.Comment: arXiv admin note: text overlap with arXiv:1612.0121
Sparse online Gaussian process adaptation for incremental backstepping flight control
Presence of uncertainties caused by unforeseen malfunctions in actuation or measurement systems or changes in aircraft behaviour could lead to aircraft loss-of-control during flight. This paper considers sparse online Gaussian Processes (GP) adaptive augmentation for Incremental Backstepping (IBKS) flight control. IBKS uses angular accelerations and control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of the relationship between inner and outer loops and control effectiveness. Proposed indirect adaptation significantly reduces model dependency. Global uniform ultimate boundness is proved for the resultant GP adaptive IBKS. Conducted research shows that if the input-affine property is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the proposed sparse GP-based estimator provides fast online identification and the resultant controller demonstrates improved stability and tracking performance
A brief review of neural networks based learning and control and their applications for robots
As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation
Gaussian process adaptive incremental backstepping flight control
The presence of uncertainties caused by unforeseen malfunctions in the actuation system or changes in aircraft behaviour could lead to aircraft loss of control during flight. The paper proposes almost model-independent control law combining recent developments in nonlinear control theory, data-driven methods, and sensor technologies by considering Gaussian Processes Adaptive augmentation for Incremental Backstepping control (IBKS) algorithm. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of control effectiveness. Conducted research shows that if the input-affine property of the IBKS is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the GP-based estimator provides fast identification and the resultant GP-adaptive IBKS algorithm demonstrates improved stability and tracking performance. The performance of the algorithm is validated using a large transport aircraft flight dynamics model
- …