Actuator fault diagnosis with neural network-integral sliding mode based unknown input observers

This paper proposes an integral sliding mode (ISM) based unknown input observer (UIO) which is able to perform fault diagnosis (FD) in condition of lack of knowledge of the plant model. In particular, a two-layer neural network (NN) is employed to estimate online the drift term of the system dynamics needed to compute the so-called integral sliding manifold. The weights of such a NN are updated online using adaptation laws directly derived from theoretical analysis, carried out in this paper. Finally, the proposal has been assessed in simulation relying on a benchmark model of a DC motor

Integral sliding modes generation via DRL-assisted Lyapunov-based control for robot manipulators

This paper proposes an enhanced version of the integral sliding mode (ISM) control, where a deep neural network (DNN) is first trained as a deep reinforcement learning (DRL) agent. Then, such a DNN is fine-tuned relying on a Lyapunov-based weight adaptation law, with the aim of compensating the lack of knowledge of the full dynamics in the case of robot manipulators. Specifically, a DRL agent is trained off-line on a reward depending on the sliding variable to estimate the unknown drift term of the robot dynamics. Such an estimate is then exploited to initialize and perform the fine tuning of the online adaptation mechanism based on the DNN. The proposal is theoretically analysed and assessed in simulation relying on the planar configuration of a Franka Emika Panda robot manipulator

Design of a deep neural network-based integral sliding mode control for nonlinear systems under fully unknown dynamics

In this letter a novel deep neural network based integral sliding mode (DNN-ISM) control is proposed for controlling perturbed systems with fully unknown dynamics. In particular, two DNNs with an arbitrary number of hidden layers are exploited to estimate the unknown drift term and the control effectiveness matrix of the system, which are instrumental to design the ISM controller. The DNNs weights are adjusted according to adaptation laws derived directly from Lyapunov stability analysis, and the proposal is satisfactorily assessed in simulation relying on benchmark examples

Deep reinforcement learning of robotic prosthesis for gait symmetry in trans-femoral amputated patients

This work proposes a novel control methodology to achieve gait symmetry in trans-femoral amputated patients with prostheses. The proposed approach allows to overcome the limits of classical model-based control strategies by introducing a Deep Reinforcement Learning (DRL) method trained ad hoc for generating the velocity control signals fed into the active lower-limb robotic prosthesis. More specifically, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to concurrently learn a Q-function and the best policy. The proposal has the advantages of being model-free and capable of adapting to different walking velocities, just requiring few measurements and without the need to online re-tune the control parameters when the human motions change. The proposed model-free approach has been tested in a realistic scenario simulated in the CoppeliaSim environment relying on gait patterns retrieved experimentally by means of markers placed on a human subject

A configuration space reference generation approach for real-time collision avoidance of industrial robot manipulators

In this work a novel approach for target reaching and collision avoidance in industrial robot manipulators is proposed. This method solely relies on the computation of the direct kinematics of the considered industrial manipulator in order to generate joint reference positions to perform real-time tracking in the operative space and avoidance of obstacles moving in the proximity of the robot. A comparison with a conventional model-based collision avoidance method has been carried out in a simulated industrial setting under different conditions, showing satisfactory results even in case of coarse sampling times. This makes the proposal suitable for filed real-time operations executed by industrial robots performing a task. The proposed approach has been deployed on the EPSON VT6 6-axis industrial manipulator, whose proprietary software has been interfaced with general-purpose robotic simulators in order to emulate complex and dynamic environments

Scenario-based collision avoidance control with deep Q-networks for industrial robot manipulators

This work proposes a scenario-based Deep Reinforcement Learning (DRL) approach enabling robot manipulators to efficiently execute industrial tasks while avoiding the collision with obstacles. The proposal exploits a DRL-based decision maker trained ad hoc so as to be able to automatically select at any time instant the most appropriate control methodology, in a given set, to execute the required industrial task. The capability of performing the selection automatically is "learnt" by training the system relying on a suitably designed reward function. It takes into account the robot relative distances from the target and the obstacles, the computational cost associated with each methodology, as well as the percentage of task completion obtained by applying the selected methodology. The learning skill is enforced by a properly sized Deep Q-Network (DQN). The proposal is assessed relying on realistic robotic manipulator scenarios reproduced in the CoppeliaSim environment

Analysis of Hybrid Cable-Thruster actuated ROV in heavy lifting interventions

Many operations performed by work class Remotely Operated Vehicles (ROVs) require the manipulation of heavy loads. An example is the manipulation and grouting of armour stones. A way to increase the working capabilities of the ROV is to introduce cables among the set of actuators. The cable lengths and tensions are controlled by winches placed on the vehicle. Being similar to a cable-driven parallel robot (CDPR), the resultant system inherits some advantages such as the possibility to generate large forces over a large workspace and the possibility to use CDPR techniques to estimate the pose of the ROV. This paper proposes a complete control architecture for the Hybrid Cable-Thruster actuated ROV (HCT-ROV) and analyzes, in computer simulations, the performances of such a system while it performs real world operations, such as heavy lifting and hovering in presence of water current