Anti-Slip AI-Driven Model-Free Control with Global Exponential Stability in Skid-Steering Robots

Undesired lateral and longitudinal wheel slippage can disrupt a mobile robot's heading angle, traction, and, eventually, desired motion. This issue makes the robotization and accurate modeling of heavy-duty machinery very challenging because the application primarily involves off-road terrains, which are susceptible to uneven motion and severe slippage. As a step toward robotization in skid-steering heavy-duty robot (SSHDR), this paper aims to design an innovative robust model-free control system developed by neural networks to strongly stabilize the robot dynamics in the presence of a broad range of potential wheel slippages. Before the control design, the dynamics of the SSHDR are first investigated by mathematically incorporating slippage effects, assuming that all functional modeling terms of the system are unknown to the control system. Then, a novel tracking control framework to guarantee global exponential stability of the SSHDR is designed as follows: 1) the unknown modeling of wheel dynamics is approximated using radial basis function neural networks (RBFNNs); and 2) a new adaptive law is proposed to compensate for slippage effects and tune the weights of the RBFNNs online during execution. Simulation and experimental results verify the proposed tracking control performance of a 4,836 kg SSHDR operating on slippery terrain.

Obstacle Avoidance Deep Reinforcement Learning-Based Trajectory Planner with Robust Low-Level Control for Robotic Manipulators

In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability and safety. To address these issues, we propose integrating an obstacle-free deep reinforcement learning (DRL) trajectory planner with a novel auto-tuning low- and joint-level control strategy, all while actively engaging in the learning phase through interactions with the environment. This approach circumvents the complexities associated with computations while also addressing nonrepetitive and random obstacle avoidance tasks. First, a model-free DRL agent to plan velocity-bounded and obstacle-free motion is employed for a manipulator with 'n' degrees of freedom (DoF) in task space through joint-level reasoning. This plan is then input into a robust subsystem-based adaptive controller, which produces the necessary torques, while the Cuckoo Search Optimization (CSO) algorithm enhances control gains to minimize the time required to reach, time taken to stabilize, the maximum deviation from the desired value, and persistent tracking error in the steady state. This approach guarantees that position and velocity errors exponentially converge to zero in an unfamiliar environment, despite unknown robotic manipulator modeling. Theoretical assertions are validated through the presentation of simulation outcomes.

Exponential Auto-Tuning Fault-Tolerant Control of N Degrees-of-Freedom Manipulators Subject to Torque Constraints

This paper presents a novel auto-tuning subsystem-based fault-tolerant control (SBFC) system designed for robot manipulator systems with n degrees of freedom. It first employs an actuator fault model to account for various faults that may occur, and second, a mathematical saturation function is incorporated to address torque constraints. Subsequently, a novel robust subsystem-based adaptive control method is proposed to direct system states to follow desired trajectories closely in the presence of input constraints, unknown modeling errors, and actuator faults, which are primary considerations of the proposed system. This ensures uniform exponential stability and sustained performance. In addition, optimal values are identified by tuning the SBFC gains and customizing the JAYA algorithm (JA), a high-performance swarm intelligence technique. Theoretical assertions are validated through the presentation of simulation outcomes.