An adaptive data-driven controller for underwater manipulators with variable payload

Adaptive Model Predictive Control for Underwater Manipulators Using Gaussian Process Regression

“…The simulation experiment in this manuscript is run in matlabR2021a, and the differential equation is calculated using the fourth-order Runge-Kutta method (ode45). The simulation task is to complete the trajectory tracking of the manipulator, the desired joint trajectory is shown as (21), and the working disturbance is given by ( 22); here, the trigonometric functions varying in a certain range is used to express the influence of unknown ocean currents on the joints of the underwater manipulator, which will be added to the joint velocity term in the actual state value. Moreover, the influence of measurement noise on the control performance is considered, Gaussian white noise with a mean of 0 and a variance of 0.001 is taken as the measurement noise and added to the output state variable at each simulation cycle.…”

“…An MPC method using neural networks has been proposed, and ensuring recursive feasibility and asymptotic stability [20]. Aiming at the degradation of the control system performance caused by external interference and different payloads, a neural network-based MPC was proposed to control the adaptive controller of the underwater manipulator [21], the neural network fits the dynamic model model based on data to make the MPC controller update its dynamic parameters.…”

Robust Control Approaches and Trajectory Planning Strategies for Industrial Robotic Manipulators in the Era of Industry 4.0: A Comprehensive Review

“…Similarly, Parkash et al [11] proposed an adaptive backstepping neural controller for a robot manipulator with dynamic uncertainties and demonstrated the controller performance using a four-DOF Barrett WAM Arm. Carlucho et al [12] developed an adaptive controller based on data-driven MPC, which utilizes a model derived using an NN, considering environmental disturbances while controlling a manipulator working with unknown payloads. Similarly, Kang et al [13] worked on NN-based MPC of a two-DOF robotic manipulator with unknown dynamics and input constraints to improve the model estimation accuracy.…”

Maximising Wrenches for Kinematically Redundant Systems with Experiments on UVMS

“…UVMS used in experiments with a 6 DOF Bluerov vehicle and 4 DOF Blueprint Lab Reach Alpha manipulator, showing vehicle pose η and end effector pose x relative to the world frame {W }. Manipulator joints are labelled q 1 to q 4 some interaction tasks [8], [9]. This reduces both operations costs and time, as well as increasing the accessibility of these systems since they can be launched, operated, and recovered by a small team with no specialised equipment.…”