PROJECT TITLE :

Online Optimal Control of Affine Nonlinear Discrete-Time Systems With Unknown Internal Dynamics by Using Time-Based Policy Update

ABSTRACT:

During this paper, the Hamilton–Jacobi–Bellman equation is solved forward-in-time for the optimal control of a class of general affine nonlinear discrete-time systems without using price and policy iterations. The proposed approach, known as adaptive dynamic programming, uses 2 neural networks (NNs), to unravel the infinite horizon optimal regulation management of affine nonlinear discrete-time systems in the presence of unknown internal dynamics and a known control coefficient matrix. One NN approximates the price function and is known as the critic NN, while the second NN generates the management input and is called the action NN. The cost perform and policy are updated once at the sampling instant and so the proposed approach can be referred to as time-based ADP. Novel update laws for tuning the unknown weights of the NNs online are derived. Lyapunov techniques are used to indicate that all signals are uniformly ultimately bounded and that the approximated management signal approaches the optimal management input with little bounded error over time. Within the absence of disturbances, an optimal management is demonstrated. Simulation results are included to show the effectiveness of the approach. The finish result's the systematic design of an optimal controller with guaranteed convergence that is appropriate for hardware implementation.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Classification of Online Toxic Comments Using Machine Learning Algorithms ABSTRACT: Toxic comments are online remarks that are insulting, abusive, or inappropriate, and frequently cause other users to quit a
PROJECT TITLE : Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation ABSTRACT: Even for humans, deciphering user buying preferences, likes and dislikes is a difficult undertaking,
PROJECT TITLE : Active Learning From Imbalanced Data A Solution of Online Weighted Extreme Learning Machine ABSTRACT: Active learning is well known for its ability to improve the quality of a classification model while also reducing
PROJECT TITLE : Online ADMM-based Extreme Learning Machine for Sparse Supervised Learning ABSTRACT: In the field of machine learning, sparse learning is a useful strategy for selecting features and avoiding overfitting. An online
PROJECT TITLE : Online Subspace Learning from Gradient Orientations for Robust Image Alignment ABSTRACT: Robust and effective picture alignment remains a difficult task due to the size and complexity of images as well as fluctuations

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry