Data-Driven Multi-step Nonlinear Model Predictive Control for Industrial Heavy Load Hydraulic Robot

TL;DR

This paper presents a data-driven nonlinear model predictive control framework using LSTM and MLP for industrial heavy load hydraulic robots, improving prediction accuracy and control efficiency through hybrid offline-online models and adaptive gradient-based optimization.

Contribution

It introduces a novel hybrid predictive model combining offline and online learning for real-time disturbance adaptation in nonlinear control of industrial robots.

Findings

Enhanced control accuracy demonstrated on a 22-ton hydraulic excavator.

Reduced computational load via single-shot multi-step prediction model.

Validated effectiveness of the approach in industrial system applications.

Abstract

Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot multi-step prediction (SSMP) model based on long short-term memory (LSTM) and multilayer perceptrons (MLP), which can directly obtain the predictive horizon without iterative repetition and reduce computational pressure. Moreover, we combine offline and online models to address disturbances stemming from environmental interactions, similar to the superposition of the robot's free and forced responses. The online model learns the system's variations from the prediction mismatches of the offline model and updates its weights in real time. The proposed hybrid predictive model simplifies the relationship between inputs and outputs into matrix multiplication, which can quickly obtain the derivative. Therefore, the solution for the control signal sequence employs a gradient descent method with an adaptive learning rate, allowing the NMPC cost function to be formulated as a convex function incorporating critical states. The learning rate is dynamically adjusted based on state errors to counteract the inherent prediction inaccuracies of neural networks. The controller outputs the average value of the control signal sequence instead of the first value. Simulations and experiments on a 22-ton hydraulic excavator have validated the effectiveness of our method, showing that the proposed NMPC approach can be widely applied to industrial systems, including nonlinear control and energy management.