Nonlinear Model Predictive Control for Internet of Things Using Particle Swarm Optimization and Cloud Computing

This paper proposes a novel model predictive control design for Internet of Things (IoT) applications by using the generalized opposition-based learning particle swarm optimization algorithm and cloud computing. The proposed nonlinear optimal control algorithm significantly generalize existing algorithms, which are tailored only for linearized or multi-model plant descriptions. The first key aim is to present a nonlinear model predictive control design based on the generalized opposition-based learning particle swarm optimization algorithm, which can stabilize the nonlinear control system without deriving the stability conditions. The second relevant contribution assumes that the complex computations involved in determining the optimal control input for proposed controller are performed by a cloud computing system, enabling efficient handling of computational tasks. Third, the communication between the actuators and transmitters in the control unit is established through IoT, ensuring seamless exchange of information. Finally, the efficacy of the proposed methodology is illustrated via simulations showcasing the implementation of distributed model predictive control on a dynamic nonlinear mobile robot model.