Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle

Abstract

This paper focuses on the trajectory tracking control problem for an articulated unmanned ground vehicle. We propose and compare two approaches in terms of performance and computational complexity. The first uses a nonlinear mathematical model derived from first principles and combines a nonlinear model predictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE) to produce a control strategy. The second is based on an input-state linearization (ISL) of the original model followed by linear model predictive control (LMPC). A fast real-time iteration scheme is proposed, implemented for the NMHE-NMPC framework and benchmarked against the ISL-LMPC framework, which is a traditional and cheap method. The experimental results for a time-based trajectory show that the NMHE-NMPC framework with the proposed real-time iteration scheme gives better trajectory tracking performance than the ISL-LMPC framework and the required computation time is feasible for real-time applications. Moreover, the ISL-LMPC produces results of a quality comparable to the NMHE-NMPC framework at a significantly reduced computational cost.

Recurrent neural network based optimal integral sliding mode tracking control for four‐wheel independently driven robots

“…In [5], an adaptive multiple low-level PID controller based double Qlearning algorithm is proposed for mobile robots. Erkan Kayacan et al propose a non-linear model predictive control based on an estimated horizon technique for an articulated unmanned ground vehicle [6]. In [7], the authors present a robust outfeedback control utilizing a mixed genetic algorithm to deal with path tracking issues for autonomous ground vehicles.…”

Observer‐based neural adaptive control of a platoon of autonomous tractor–trailer vehicles with uncertain dynamics

“…Among them, one attractive solution is the virtual leader‐following in which a virtual leader determines directions, speeds, and accelerations of all following vehicles, and all robots in the convoy respond to the leader vehicle's action. WMRs [4–6] are classified into several categories based on their mobility and steerability where car‐like ones and autonomous tractor–trailer WMRs (TTWMRs) are not paid attention enough in the literature [7–9]. Unfortunately, few controllers are proposed for the formation of such multibody vehicles [9] in recent years and most of the presented controllers are devoted to a single TTWMR.…”

“…Then, a saturated filtered error variable is defined to design our controller while bounding the unconstrained errors as well. After a deep literature review including [1–22], the main novelties of the proposed controller are listed as follows: (1) This is the first attempt to design a tracking controller for a convoy‐like motion of multiple off‐axle hitching tractor–trailers. (2) The proposed controller does not require the velocity and acceleration measurements in real‐time. (3) The actuator saturation risk is reduced by considering a hyperbolic tangent function and projection‐type neural adaptive rules. (4) The collisions between successive tractor–trailers are avoided by constraining the relative distance error. (5) The consecutive vehicles' connectivity is ensured by considering the limited communication range of vehicle transmitter–receivers. (6) The possible singularity of the tracking controller is avoided by constraining the relative angle error. (7) The desired prescribed transient and steady‐state performance criteria are guaranteed in advance. (8) All types of TTWMR model uncertainties are compensated by neural adaptive robust techniques. …”

Closed-Loop Error Learning Control for Uncertain Nonlinear Systems With Experimental Validation on a Mobile Robot

“…Modeled and identified uncertainties partially improved mobile robot path tracking performance; however, it was not sufficient. To model and identify uncertainties fully, a framework consisting of moving horizon estimation and model predictive control methods was proposed for articulated unmanned ground vehicles [26]- [28] and mobile robots [29], [30]. In these works, an adaptive kinematic model was derived by adding traction parameters for longitudinal and side slips into the traditional kinematic model.…”