5,198 research outputs found
A metaheuristic particle swarm optimization approach to nonlinear model predictive control
This paper commences with a short review on
optimal control for nonlinear systems, emphasizing the Model
Predictive approach for this purpose. It then describes the Particle Swarm Optimization algorithm and how it could be applied
to nonlinear Model Predictive Control. On the basis of these
principles, two novel control approaches are proposed and anal-
ysed. One is based on optimization of a numerically linearized
perturbation model, whilst the other avoids the linearization step
altogether. The controllers are evaluated by simulation of an
inverted pendulum on a cart system. The results are compared
with a numerical linearization technique exploiting conventional
convex optimization methods instead of Particle Swarm Opti-
mization. In both approaches, the proposed Swarm Optimization
controllers exhibit superior performance. The methodology is
then extended to input constrained nonlinear systems, offering a
promising new paradigm for nonlinear optimal control design.peer-reviewe
Gaussian process based model predictive control : a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering, School of Engineering and Advanced Technology, Massey University, New Zealand
The performance of using Model Predictive Control (MPC) techniques is highly dependent
on a model that is able to accurately represent the dynamical system. The datadriven
modelling techniques are usually used as an alternative approach to obtain such
a model when first principle techniques are not applicable. However, it is not easy to
assess the quality of learnt models when using the traditional data-driven models, such
as Artificial Neural Network (ANN) and Fuzzy Model (FM). This issue is addressed in
this thesis by using probabilistic Gaussian Process (GP) models.
One key issue of using the GP models is accurately learning the hyperparameters.
The Conjugate Gradient (CG) algorithms are conventionally used in the problem of
maximizing the Log-Likelihood (LL) function to obtain these hyperparameters. In this
thesis, we proposed a hybrid Particle Swarm Optimization (PSO) algorithm to cope with
the problem of learning hyperparameters. In addition, we also explored using the Mean
Squared Error (MSE) of outputs as the fitness function in the optimization problem.
This will provide us a quality indication of intermediate solutions.
The GP based MPC approaches for unknown systems have been studied in the past
decade. However, most of them are not generally formulated. In addition, the optimization
solutions in existing GP based MPC algorithms are not clearly given or are
computationally demanding. In this thesis, we first study the use of GP based MPC approaches
in the unconstrained problems. Compared to the existing works, the proposed
approach is generally formulated and the corresponding optimization problem is eff-
ciently solved by using the analytical gradients of GP models w.r.t. outputs and control
inputs. The GPMPC1 and GPMPC2 algorithms are subsequently proposed to handle
the general constrained problems. In addition, through using the proposed basic and
extended GP based local dynamical models, the constrained MPC problem is effectively
solved in the GPMPC1 and GPMPC2 algorithms. The proposed algorithms are verified
in the trajectory tracking problem of the quadrotor.
The issue of closed-loop stability in the proposed GPMPC algorithm is addressed
by means of the terminal cost and constraint technique in this thesis. The stability
guaranteed GPMPC algorithm is subsequently proposed for the constrained problem. By
using the extended GP based local dynamical model, the corresponding MPC problem
is effectively solved
Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints
Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance
Nonlinear model predictive control of an Organic Rankine Cycle for exhaust waste heat recovery in automotive engines
Energy recovery from exhaust gas waste heat can be regarded as an effective way to improve the energy efficiency of automotive powertrains, thus reducing CO2 emissions. The application of Organic Rankine Cycles (ORCs) to waste heat recovery is a solution that couples effectiveness and reasonably low technological risks. On the other hand, ORC plants are rather complex to design, integrate and control, due to the presence of heat exchangers operating with phase changing fluid, and several control devices to regulate the thermodynamic states of the systems. Furthermore, the power output and efficiency of ORC systems are extremely sensitive to the operating conditions, requiring precise control of the evaporator pressure and superheat temperature. This paper presents an optimization and control design study for an Organic Rankine Cycle plant for automotive engine waste heat recovery. The analysis has been developed using a detailed Moving Boundary Model that predicts mass and energy flows through the heat exchangers, valves, pumps and expander, as well as the system performance. Starting from the model results, a nonlinear model predictive controller is designed to optimize the transient response of the ORC system. Simulation results for an acceleration-deceleration test illustrate the benefits of the proposed control strategy
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Decision trees usefully represent sparse, high dimensional and noisy data.
Having learned a function from this data, we may want to thereafter integrate
the function into a larger decision-making problem, e.g., for picking the best
chemical process catalyst. We study a large-scale, industrially-relevant
mixed-integer nonlinear nonconvex optimization problem involving both
gradient-boosted trees and penalty functions mitigating risk. This
mixed-integer optimization problem with convex penalty terms broadly applies to
optimizing pre-trained regression tree models. Decision makers may wish to
optimize discrete models to repurpose legacy predictive models, or they may
wish to optimize a discrete model that particularly well-represents a data set.
We develop several heuristic methods to find feasible solutions, and an exact,
branch-and-bound algorithm leveraging structural properties of the
gradient-boosted trees and penalty functions. We computationally test our
methods on concrete mixture design instance and a chemical catalysis industrial
instance
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
A Nonlinear System Identification Method Based on Adaptive Neural Network
Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network
In this study, an artificial neural network (ANN) based on particle swarm
optimization (PSO) was developed for the time series prediction. The hybrid
ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the
short-term . The performance prediction was evaluated and compared with
another studies available in the literature. Also, we presented properties of
the dynamical system via the study of chaotic behaviour obtained from the
predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with
a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in
order to obtain a new estimator of the predictions, which also allowed us to
compute uncertainties of predictions for noisy Mackey--Glass chaotic time
series. Thus, we studied the impact of noise for several cases with a white
noise level () from 0.01 to 0.1.Comment: 11 pages, 8 figure
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