27,446 research outputs found
Model-free neural network-based predictive control for robust operation of power converters
An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC
Neurodynamic approaches to model predictive control.
Pan, Yunpeng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 98-107).Abstract also in Chinese.Abstract --- p.ip.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.2Chapter 1.1 --- Model Predictive Control --- p.2Chapter 1.2 --- Neural Networks --- p.3Chapter 1.3 --- Existing studies --- p.6Chapter 1.4 --- Thesis structure --- p.7Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9Chapter 2.1 --- Problem Formulation --- p.9Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10Chapter 2.1.2 --- Linear Programming Formulation --- p.13Chapter 2.2 --- Neural Network Approaches --- p.15Chapter 2.2.1 --- Neural Network Model 1 --- p.15Chapter 2.2.2 --- Neural Network Model 2 --- p.16Chapter 2.2.3 --- Control Scheme --- p.17Chapter 2.3 --- Simulation Results --- p.18Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22Chapter 3.1 --- Problem Formulation --- p.22Chapter 3.2 --- A Neural Network Approach --- p.25Chapter 3.2.1 --- The Simplified Dual Network --- p.26Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28Chapter 3.3 --- Simulation Results --- p.28Chapter 3.3.1 --- Example 1 --- p.28Chapter 3.3.2 --- Example 2 --- p.29Chapter 3.3.3 --- Example 3 --- p.33Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36Chapter 4.1 --- Problem Formulation --- p.36Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40Chapter 4.2.1 --- Neural Network Model --- p.40Chapter 4.2.2 --- Learning Algorithm --- p.41Chapter 4.2.3 --- Control Scheme --- p.41Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42Chapter 4.3.1 --- Example 1 --- p.44Chapter 4.3/2 --- Example 2 --- p.44Chapter 4.3.3 --- Example 3 --- p.46Chapter 4.3.4 --- Example 4 --- p.48Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50Chapter 5.1 --- MPC System Description --- p.51Chapter 5.1.1 --- Model Predictive Control --- p.51Chapter 5.1.2 --- Dynamical System Identification --- p.52Chapter 5.2 --- Problem Formulation --- p.54Chapter 5.3 --- Dynamic Optimization --- p.58Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60Chapter 5.3.3 --- Convergence Analysis --- p.61Chapter 5.4 --- RNN-based MPC Scheme --- p.65Chapter 5.5 --- Simulation Results --- p.67Chapter 5.5.1 --- Example 1 --- p.67Chapter 5.5.2 --- Example 2 --- p.68Chapter 5.5.3 --- Example 3 --- p.76Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81Chapter 6.1 --- Problem Formulation --- p.82Chapter 6.1.1 --- Process Model --- p.82Chapter 6.1.2 --- Robust. MPC Design --- p.82Chapter 6.2 --- Recurrent Neural Network Approach --- p.86Chapter 6.2.1 --- Neural Network Model --- p.86Chapter 6.2.2 --- Convergence Analysis --- p.88Chapter 6.2.3 --- Control Scheme --- p.90Chapter 6.3 --- Simulation Results --- p.91Chapter 7 --- Summary and future works --- p.95Chapter 7.1 --- Summary --- p.95Chapter 7.2 --- Future works --- p.96Bibliography --- p.9
Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control
(NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future dynamic behavior
of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentiation (AD) techniques. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the
network but also produces the sensitivity for the training problem. The same approach is also used to solve the online optimization problem in the predictive controller. The proposed neural network and the nonlinear predictive controller were tested on an evaporation case
study. A good model fitting for the nonlinear plant is obtained using the new method. A comparison with other approaches shows that
the new algorithm can considerably reduce network training time and improve solution accuracy. The CTRNN trained is used as an
internal model in a predictive controller and results in good performance under different operating conditions
Autonomous Autorotation of a Tilt-Rotor Aircraft Using Model Predictive Control
Tilt rotor vehicles are governed by FAA laws also used for conventional helicopters, which require autorotational maneuvering and landing given a total power failure. With low inertia rotors and high disk loading of tilt rotor vehicles, this already difficult task becomes significantly more challenging. In this work, a model predictive controller is developed to autonomously maneuver and land a tilt rotor given complete power loss. A high fidelity model of a tilt rotor vehicle is created and used to simulate the vehicle dynamics and response to control inputs. A reduced order dynamic model is used within a model predictive control algorithm to predict vehicle states on a receding horizon and optimize the control inputs. Constraint and cost functions are designed to promote reliable nonlinear optimization using a recurrent neural network. Simulation results show that the controller works in both normal operation states and in power-off autorotation
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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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Economic MPC of Nonlinear Processes via Recurrent Neural Networks Using Structural Process Knowledge
This work discusses three methods that incorporate a priori process knowledge into recurrent neural network (RNN) modeling of nonlinear processes to get increased prediction accuracy and provide information on how the neural network models are structured. The first method proposes a hybrid model that integrates first-principles models and RNN models together. The second method proposes a partially-connected RNN model which its structure is based on a priori structural process knowledge. The third method proposes a weight-constrained RNN model that integrates weight constraints into the training of the RNN model. The proposed RNN models are used in an economic model predictive control system and then applied to a chemical process example to validate the improved approximation performance compared to a fully-connected RNN model that is treated as a black box model
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precluding its analytic representation inside an RNN. In the
Predictive-State Representation (PSR) literature, latent state processes are
modeled by an internal state representation that directly models the
distribution of future observations, and most recent work in this area has
relied on explicitly representing and targeting sufficient statistics of this
probability distribution. We seek to combine the advantages of RNNs and PSRs by
augmenting existing state-of-the-art recurrent neural networks with
Predictive-State Decoders (PSDs), which add supervision to the network's
internal state representation to target predicting future observations.
Predictive-State Decoders are simple to implement and easily incorporated into
existing training pipelines via additional loss regularization. We demonstrate
the effectiveness of PSDs with experimental results in three different domains:
probabilistic filtering, Imitation Learning, and Reinforcement Learning. In
each, our method improves statistical performance of state-of-the-art recurrent
baselines and does so with fewer iterations and less data.Comment: NIPS 201
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