Ingenium 2021
Neural Network-based approximation of model predictive control applied to a flexible shaft servomechanism Yuanwu He, Yuliang Xiao and Nikhil Bajaj Department of Mechanical Engineering and Materials Science Yuliang Xiao is from Beijing, China. His interests in robotics and strong education background motivate him to take further research on Robotics and Computer Vision in graduate.
Yuliang Xiao
Yuanwu He was born and raised in Beijing, China. He is passionate about system controls and robotics, which are also the fields he would like to dive in for his career of engineering.
Yuanwu He
Nikhil Bajaj, Ph.D.
Dr. Bajaj is an Assistant Professor in the Department of Mechanical Engineering and Materials Science. He received his PhD from Purdue University in 2017 and served as a postdoctoral researcher from 2017-2019. His current research interests include nonlinear dynamics, mechatronics, advanced sensors, and advanced manufacturing.
Significance Statement
This work seeks to address the computational burden associated with Model Predictive Control (MPC) algorithms, and specifically demonstrates the improvement of the memory footprint of an explicit MPC via the use of feedforward neural networks for approximation of the controller.
Category: Computational Research
Keyword: model predictive control (MPC), machine learning, feed-forward neural network, computational cost.
Abstract
In this paper, we seek to address the computational burden associated with Model Predictive Control (MPC) algorithms. MPC algorithms seek an optimal control action over a control horizon that drives a discrete time control system according to the determined reference. It is always used in the process industries in chemical plants, oil refineries, power system balancing models and power electronics. One critical limitation of MPC is the computational complexity, which constrains a system’s ability to find an optimal solution in real-time, resulting in limitations on the classes of systems for which MPC can be used. To address this problem, the major findings are to develop explicit MPC (eMPC) algorithms, which precompute the optimal solutions across the state, reference, and input space, and interpolate between optimal solutions via different methods. In general, however, eMPCs are characterized by trading off computational burden in real-time for memory allocation size [1]. In this work we present the successful use of neural networks to approximate a MPC control algorithm for a particular servomechanism and evaluate their performance and utility.
1. Introduction
Model Predictive Control (MPC) is a popular family of control algorithms in a wide and expanding variety of fields [2]. MPC finds the optimal control action over a control horizon that drives a discrete time control system output to the determined reference while honoring the system constraints. Hence, it is a good tool to handle multi-input multi-output systems that interact with inputs and outputs since designing this system by using traditional controllers like PID is challenging. MPC can help solve constraints because it has predictive capabilities, which computes the trajectories of each state in advance in order to select the best control action according to a cost function. This is why engineers often use MPC to get detailed control of each state during a dynamic process, such as chemical plants, oil refineries, and power balancing systems. The optimization problem iteratively evaluates the expected system response at each step over a prediction horizon. Compared to trajectory planning in the task space or the determination of required control commands in the state space [2], MPC is an advanced technique and can generally provide good performance. The basic MPC algorithm can be computationally expensive. When the prediction horizon is large, a limitation of this approach is its computational burden since the solutions need to be computed in real-time between control actions. This restricts the applicability of MPC to systems that have large time constants and/or high computational power [1]. Decreasing computational cost is a desirable objective, as it will allow for MPC to be practical in more systems. A common way of addressing this is with an “explicit” MPC (eMPC), which trades off memory for computational complexity. The explicit MPC is a way to manage the computational load by pre-computing the optimal control law u* = μ*(x) offline as a function of all feasible states x [3]. 107
