An efficient application of particle swarm optimization in model predictive control of constrained two-tank system

Abstract

<span>Despite all the model predictive control (MPC) based solution advantages such as a guarantee of stability, the main disadvantage such as an exponential growth of the number of the polyhedral region by increasing the prediction horizon exists. This causes the increment in computation complexity of control law. In this paper, we present the efficiency of particle swarm optimization (PSO) in optimal control of a two-tank system modeled as piecewise affine. The solution of the constrained final time-optimal control problem (CFTOC) is derived, and then the PSO algorithm is used to reduce the computational complexity of the control law and set the physical parameters of the system to improve performance simultaneously. On the other hand, a new combined algorithm based on PSO is going to be used to reduce the complexity of explicit MPC-based solution CFTOC of the two-tank system; consequently, that the number of polyhedral is minimized, and system performance is more desirable simultaneously. The proposed algorithm is applied in simulation and our desired subjects are reached. The number of control law polyhedral reduces from 42 to 10 and the liquid height in both tanks reaches the desired certain value in 189 seconds. Search time and apply control law in 25 seconds.</span>

Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability

“…where {𝑢(𝑡)} 0≤𝑡≤𝑁−1 = {𝑢(0), 𝑢(1), ⋯ , 𝑢(𝑁 − 1)} and 𝑁 ≥ 1 is the finite horizon. The fuzzy objective function 𝐽 ̄0,𝑁 (𝑥(0), {𝑢(𝑡)} 0≤𝑡≤𝑁−1 ) with a finite horizon is given by (6).…”