5,233 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
Solution of Linear Programming Problems using a Neural Network with Non-Linear Feedback
This paper presents a recurrent neural circuit for solving linear programming problems. The objective is to minimize a linear cost function subject to linear constraints. The proposed circuit employs non-linear feedback, in the form of unipolar comparators, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit compares favorably with other proposed circuits for the same task. PSPICE simulation results are presented for a chosen optimization problem and are found to agree with the algebraic solution. Hardware test results for a 2–variable problem further serve to strengthen the proposed theory
Design optimization applied in structural dynamics
This paper introduces the design optimization strategies, especially for structures which have dynamic constraints. Design optimization involves first the modeling and then the optimization of the problem. Utilizing the Finite Element (FE) model of a structure directly in an optimization process requires a long computation time. Therefore the Backpropagation Neural Networks (NNs) are introduced as a so called surrogate model for the FE model. Optimization techniques mentioned in this study cover the Genetic Algorithm (GA) and the Sequential Quadratic Programming (SQP) methods. For the applications of the introduced techniques, a multisegment cantilever beam problem under the constraints of its first and second natural frequency has been selected and solved using four different approaches
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