The intelligent critic framework for advanced optimal control

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming &#x0028 ADP &#x0029 technique is utilized to implement the optimal parallel tracking control using a critic neural network &#x0028 NN &#x0029 to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded &#x0028 UUB &#x0029 . Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases.

Parallel control for optimal tracking via adaptive dynamic programming

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels. 

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.

A novel event-triggered optimal tracking control (ETOTC) method is developed for discrete-time nonlinear systems in this study. For the time-invariant desired trajectory, we prove that the tracking error is asymptotically stable, and an upper bound of the real performance index can be predetermined by a design parameter. For the time-varying desired trajectory, the developed triggering condition reduces communication costs by relaxing the restriction of the asymptotic stability of the closed-loop system, and we prove that the tracking error is uniformly ultimately bounded (UUB). The developed ETOTC method entails obtaining the next state of the real system. Therefore, a parallel control approach is proposed to predict the next state by constructing a parallel system for the real system. Neural networks (NNs) and adaptive dynamic programming (ADP) techniques are utilized in the parallel control approach. Moreover, the stability analysis of the closed-loop system is shown, and the tracking error and NN weight estimation errors are proved to be UUB using the Lyapunov approach. Finally, we validate the developed ETOTC method through two simulations.

Event-Triggered Optimal Parallel Tracking Control for Discrete-Time Nonlinear Systems

In this article, a new self-learning parallel control method, which is based on adaptive dynamic programming (ADP) technique, is developed for solving the optimal control problem of discrete- time time-varying nonlinear systems. It aims to obtain an approximate optimal control law sequence and simultaneously guarantees the convergence of the value function. Establishing the time-varying artificial system by neural networks in a certain time-horizon, a control-sequence-improvement ADP algorithm is developed to obtain the control law sequence. For the first time, the criteria of the parallel execution are presented, such that the value function is proven to converge to a finite neighborhood of the optimal performance index function. Finally, numerical results and analysis are presented to demonstrate the effectiveness of the parallel control method.

Jingwei Lu

Papers

Parallel control for optimal tracking via adaptive dynamic programming

Event-Triggered Optimal Parallel Tracking Control for Discrete-Time Nonlinear Systems

Discrete-Time Self-Learning Parallel Control

Event-Triggered Optimal Control for Discrete-Time Multi-Player Non-Zero-Sum Games Using Parallel Control

A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems