Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems

Abstract

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

Probabilistic Framework Allocation on Underwater Vehicular Systems Using Hydrophone Sensor Networks

“…Then, utilising the same wireless stations, advancements in robotic technology were produced [13], and the behaviour of cloud technology for watching such transitions with industry connections resulted in reduced resource allocation. The next step was to develop a self-triggered model using the predictive control technique [14]. The predictive control approach was created to provide closed loop system solutions using time reduction tactics.…”

“…Technology Developed Purpose of Development [11] Wireless solar boosting Navigation monitoring [12] Robotic technology Maximizing the depth of monitoring systems [13] Robotic technology with wireless stations Minimizing the resource allocation [14] Predictive control Control of closed loop systems [16] Decision-making approach Monitoring the physical condition of aqua faunas [19] Microelectromechanical systems (MEMS)…”

Design of a Model Predictive Trajectory Tracking Controller for Mobile Robot Based on the Event-Triggering Mechanism

“…Expand equation (2) by Taylor series at any reference point (ξ r , u r ) and retain only the first-order terms and ignore the higher-order terms. e following equation can be obtained:…”

“…In recent years, many research achievements have been made on the trajectory tracking control of mobile robots. e mainstream tracking control algorithms at this stage included sliding mode control, model predictive control (MPC), and optimal control [1][2][3][4]. Due to the advantages in handling system constraints and balancing multiple control objectives, MPC has been applied in more and more practical mobile robots, such as wheeled robots [5][6][7][8][9] and underwater autonomous navigation robots [10,11].…”