81,069 research outputs found
Parallel Shooting Sequential Quadratic Programming for Nonlinear MPC Problems
In this paper, we propose a parallel shooting algorithm for solving nonlinear
model predictive control problems using sequential quadratic programming. This
algorithm is built on a two-phase approach where we first test and assess
sequential convergence over many initial trajectories in parallel. However, if
none converge, the algorithm starts varying the Newton step size in parallel
instead. Through this parallel shooting approach, it is expected that the
number of iterations to converge to an optimal solution can be decreased.
Furthermore, the algorithm can be further expanded and accelerated by
implementing it on GPUs. We illustrate the effectiveness of the proposed
Parallel Shooting Sequential Quadratic Programming (PS-SQP) method in some
benchmark examples for nonlinear model predictive control. The developed PS-SQP
parallel solver converges faster on average and especially when significant
nonlinear behaviour is excited in the NMPC horizon.Comment: 7 pages, 6 figures, submitted and accepted for the 7th IEEE
Conference on Control Technology and Applications (CCTA) 202
Preconditioned Continuation Model Predictive Control
Model predictive control (MPC) anticipates future events to take appropriate
control actions. Nonlinear MPC (NMPC) describes systems with nonlinear models
and/or constraints. A Continuation/GMRES Method for NMPC, suggested by T.
Ohtsuka in 2004, uses the GMRES iterative algorithm to solve a forward
difference approximation of the Continuation NMPC (CNMPC) equations on
every time step. The coefficient matrix of the linear system is often
ill-conditioned, resulting in poor GMRES convergence, slowing down the on-line
computation of the control by CNMPC, and reducing control quality. We adopt
CNMPC for challenging minimum-time problems, and improve performance by
introducing efficient preconditioning, utilizing parallel computing, and
substituting MINRES for GMRES.Comment: 8 pages, 6 figures. To appear in Proceedings SIAM Conference on
Control and Its Applications, July 8-10, 2015, Paris, Franc
Multivariable nonlinear advanced control of copolymerization processes
A reliable multivariable model of a process is a fundamental prerequisite for the design of an efficient control strategy. Though, such a model is often very hard to obtain via a first-principles approach. The development of two fuzzy model-based multivariable nonlinear predictive control schemes and their implementation on a copolymerization process are described in this paper. Multi-input/single-output models are developed using fuzzy logic and combined to form a parallel system model for simulation and online prediction. The behavior of the outlined controllers were compared to the dynamic matrix control (DMC) and to a typical nonlinear model-based predictive control (NMPC) for regulatory problem and the obtained results showed the effectiveness of the proposed structures
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
ParNMPC – a parallel optimisation toolkit for real-time nonlinear model predictive control
Real-time optimisation for nonlinear model predictive control (NMPC) has always been challenging, especially for fast-sampling and large-scale applications. This paper presents an efficient implementation of a highly parallelisable method for NMPC, called ParNMPC. The implementation details of ParNMPC are introduced, including a dedicated discretisation method suitable for parallelisation, a framework that unifies search direction calculation done using Newton's method and the parallel method, line search methods for guaranteeing convergence, and a warm start strategy for the interior-point method. To assess the performance of ParNMPC under different configurations, three experiments including a closed-loop simulation of a quadrotor, a real-world control example of a laboratory helicopter and a closed-loop simulation of a robot manipulator are shown. These experiments show the effectiveness and efficiency of ParNMPC both in serial and parallel
Towards parallelizable sampling-based Nonlinear Model Predictive Control
This paper proposes a new sampling-based nonlinear model predictive control
(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
N and linear in the number of samples. The idea of the proposed algorithm is to
use the sequence of predicted inputs from the previous time step as a warm
start, and to iteratively update this sequence by changing its elements one by
one, starting from the last predicted input and ending with the first predicted
input. This strategy, which resembles the dynamic programming principle, allows
for parallelization up to a certain level and yields a suboptimal nonlinear MPC
algorithm with guaranteed recursive feasibility, stability and improved cost
function at every iteration, which is suitable for real-time implementation.
The complexity of the algorithm per each time step in the prediction horizon
depends only on the horizon, the number of samples and parallel threads, and it
is independent of the measured system state. Comparisons with the fmincon
nonlinear optimization solver on benchmark examples indicate that as the
simulation time progresses, the proposed algorithm converges rapidly to the
"optimal" solution, even when using a small number of samples.Comment: 9 pages, 9 pictures, submitted to IFAC World Congress 201
Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle
This paper presents a nonlinear-model based hybrid optimal control technique
to compute a suboptimal power-split strategy for power/energy management in a
parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained
as model predictive control solution to the power management control problem
(PMCP) of the PHEV, i.e., to decide upon the power distribution among the
internal combustion engine, an electric drive, and other subsystems. A
hierarchical control structure of the hybrid vehicle, i.e., supervisory level
and local or subsystem level is assumed in this study. The PMCP consists of a
dynamical nonlinear model, and a performance index, both of which are
formulated for power flows at the supervisory level. The model is described as
a bi-modal switched system, consistent with the operating mode of the electric
ED. The performance index prescribing the desired behavior penalizes vehicle
tracking errors, fuel consumption, and frictional losses, as well as sustaining
the battery state of charge (SOC). The power-split strategy is obtained by
first creating the embedded optimal control problem (EOCP) from the original
bi-modal switched system model with the performance index. Direct collocation
is applied to transform the problem into a nonlinear programming problem. A
nonlinear predictive control technique (NMPC) in conjunction with a sequential
quadratic programming solver is used to compute suboptimal numerical solutions
to the PMCP. Methods for approximating the numerical solution to the EOCP with
trajectories of the original bi-modal PHEV are also presented in this paper.
The usefulness of the approach is illustrated via simulation results on several
case studies
Bilevel optimization for bunching mitigation and eco-driving of electric bus lines
The problems of bus bunching mitigation and of the energy management of groups of vehicles are traditionally treated separately in the literature, and formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program. Since this nonlinear program only has few coupling variables, it is shown how it can be solved in parallel aboard each bus such that only a marginal amount of computations need to be carried out centrally. This leverages the decentralized structure of a bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in a model predictive control. Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. Our method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines
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Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance
This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances
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