Model-Free Optimal Control for Affine Nonlinear Systems With Convergence Analysis


In this paper, a self-learning management scheme is proposed for the infinite horizon optimal management of affine nonlinear systems based on the action dependent heuristic dynamic programming algorithm. The policy iteration technique is introduced to derive the optimal control policy with feasibility and convergence analysis. It shows that the “greedy” management action for every state is uniquely existent, the learned management policy when each policy iteration is admissible, and also the optimal management policy is ready to be obtained. 2 3-layer perceptron neural networks are employed to implement the scheme. The critic network is trained by a completely unique rule to evolve to the Bellman equation, and the action network is trained to yield a higher management policy. Each coaching processes alternate until the optimal management policy is achieved. Two simulation examples are provided to validate the effectiveness of the approach. Note to Practitioners - The objective of planning optimal controllers without mathematical models is sought by control practitioners, whereas existing approaches usually derive optimal controllers by accessing the mathematical models or identified models. This paper proposes a brand new approach which derives optimal controllers by numerical iteration method without accessing any information of the mathematical models. It offers evaluation for each state-action try in the full state-action house through the collected knowledge of the underlying system, and then selects the action with the most effective evaluation for each state. What is needed initial admissible control policy. Theorems show that optimal controllers will be acquired and simulation studies verify effectiveness. Further research will extend this approach to on-line self-learning optimal control approach, so it will adapt the variation of underlying systems.

Did you like this research project?

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

PROJECT TITLE : Semi-Decentralized Network Slicing for Reliable V2V Service Provisioning A Model-free Deep ABSTRACT: When it comes to supporting new Vehicle-to-Vehicle (V2V) applications that have a variety of quality of service
PROJECT TITLE : Attention in Reasoning Dataset, Analysis, and Modeling ABSTRACT: Although attention has become an increasingly popular component in deep neural networks for the purpose of both interpreting data and improving
PROJECT TITLE : AdaPool: A Diurnal-Adaptive Fleet Management Framework Using Model-Free Deep Reinforcement Learning and Change Point Detection ABSTRACT: In this paper, an adaptive model-free deep reinforcement approach is presented.
PROJECT TITLE : A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning ABSTRACT: The proliferation of self-driving technology, ridesharing platforms, and autonomous vehicles will bring
PROJECT TITLE : Estimation, Control and Prediction of Voltage Level and Stability at Receiving Node ABSTRACT: Receiver voltage stability is addressed in this article. Voltage stability and level are intertwined concepts. Although

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

Project Enquiry