33,486 research outputs found

    Neural Network Predictive Control (NNPC) of a Deep Submergence Rescue Vehicle (DSRV)

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    In this paper, the modeling and design of the depth control systems using Neural Network Predictive Control (NNPC)for a small unmanned underwater vehicle (UUV) will be described. Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an UUV is described in this paper. The main purpose of the underwater vehicle is that the vehicle must be stable over the entire range of operation. These techniques have the purpose of ensuring zero steady state error and minimum error in response to step commands in the desired depth.The depth performance for NNPC is discussed in terms of error and execution time. This NNPC will be compared with conventional controller such as PD controller and also by using the Fuzzy Logic Controller (FLC). For the comparison of computational time between this controllers, it can be observed that Fuzzy Logic is faster and neural network predictive controller is the slowest between them. It has been shown that the neural network predictive controller improved the transient response and error measure which shows the effectiveness of the designed controller

    Neural Network Based Predictive Control for Nonlinear Systems

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    A neural network based predictive controller design algorithm is introduced for nonlinear control systems. It is shown that the use of nonlinear programming techniques can be avoided by using a set of affine nonlinear predictors to predict the output of the nonlinear process. The new predictive controller, based on this design, is both simple and easy to implement in practice. An on-line weight learning algorithm based on neural networks is introduced and convergence of both the weights and estimation errors is established. Predictive controller design, based on the new procedure, is illustrated using a growing network example

    Dual Heuristic Dynamic Programing Control of Grid-Connected Synchronverters

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    A new approach to control a grid-connected synchronverter by using a dual heuristic dynamic programing (DHP) design is presented. The disadvantages of conventional synchronverter controller such as the challenges to cope with nonlinearity, uncertainties, and non-inductive grids are discussed. To deal with the aforementioned challenges a neural network–based adaptive critic design is introduced to optimize the associated cost function. The characteristic of the neural networks facilitates the performance under uncertainties and unknown parameters (e.g. different power angles). The proposed DHP design includes three neural networks: system NN, action NN, and critic NN. The simulation results compare the performance of the proposed DHP with a traditional PI-based design and with a neural network predictive controller. It is shown a well-trained DHP design performs in a trajectory, which is more optimal compared to the other two controllers

    Learning an Approximate Model Predictive Controller with Guarantees

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    A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding's Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.Comment: 6 pages, 3 figures, to appear in IEEE Control Systems Letter

    Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

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    This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity

    Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

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    Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

    Neural Network Approach for Obstacle Avoidance in 3-D Environments for UAVs

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    In this paper a controller design is proposed to get obstacle free trajectories in a three dimensional urban environment for unmanned air vehicles (UAVs). The controller has a two-layer architecture. In the upper layer, vision-inspired Grossberg neural network is proposed to get the shortest distance paths. In the bottom layer, a model predictive control (MPC) based controller is used to obtain dynamically feasible trajectories. Simulation results are presented for to demonstrate the potential of the approach
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