Observer-Based Approximate Affine Nonlinear Model Predictive Controller for Hydraulic Robotic Excavators with Constraints

Abstract

Given the highly nonlinear and strongly constrained nature of the electro-hydraulic system, we proposed an observer-based approximate nonlinear model predictive controller (ANMPC) for the trajectory tracking control of robotic excavators. A nonlinear non-affine state space equation with identified parameters is employed to describe the dynamics of the electro-hydraulic system. Then, to mitigate the plant-model mismatch caused by the first-order linearization, an approximate affine nonlinear state space model is utilized to represent the explicit relationship between the output and input and an ANMPC is designed based on the approximate nonlinear model. Meanwhile, the Extended Kalman Filter was introduced for state observation to deal with the unmeasurable velocity information and heavy measurement noises. Comparative experiments are conducted on a 1.7-ton hydraulic robotic excavator, where ANMPC and linear model predictive control are used to track a typical excavation trajectory. The experimental results provide evidence of convincing trajectory tracking performance.

An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian

“…As a key technique in nonlinear recursive filtering, the Extended Kalman Filter (EKF) is widely recognized and utilized [16,26,27]. Its popularity stems from several advantages, such as obviating the need for calculating nominal trajectories, its straightforward methodology, and ease of implementation.…”

Trajectory Tracking Control of a Skid-Steer Mobile Robot Based on Nonlinear Model Predictive Control with a Hydraulic Motor Velocity Mapping

“…However, these control methods have limitations in dealing with various physical constraints, such as actuator power constraints, velocity and acceleration limitations, and so on [15]. Neglecting these physical constraints in control strategies can potentially degrade control performance and even result in system instability [16].…”

“…The process of parameter identification is often time-consuming and expensive [26]. Additionally, due to cost and installation constraints, there may not be enough sensors available on the actual system to collect the data required for parameter identification [16]. Therefore, there is a need for a control scheme that does not rely on an explicit mechanistic model.…”