Affordable Control Platform with MPC Application

Abstract

This paper presents a control platform developed to interface with various hardware, allowing the design and rapid implementation even of advanced controllers, both on academic and industrial systems. The code of the controllers is written in the open-source Python language, facilitating the translation of code usually written in commercial software. The proposed platform can use from Arduinos to Programmable Logic Computers (PLCs). Beyond the research and tests on industrial facilities, the simplicity of the proposed platform allows its use also for educational and training purposes. Therefore, the proposed platform can help students focus on system analysis and control theory instead of hardware interfacing issues, while using low cost hardware. Developed in a client-server scheme, the platform can run in affordable computers while taking advantage of high-level mathematical and graphical tools available in Python language, allowing rapid implementation of advanced controllers. The use of this platform is illustrated with an implementation of a model predictive control (MPC) of a level control in a laboratory-scale process. A PLC is used to take the level measures, to dispatch control signals, and also for interlocking secure tasks. The controller runs on a Raspberry Pi computer that communicates with the PLC through an ethernet link.

Estimação de falhas com observadores de entrada desconhecida e modelagem fuzzy granular

“…(Oliveira et al, 2020). O controlador do processo opera em uma Raspberry Pi 3 conectada ao controlador lógico programável (CLP) Simatic S7-300, conectados via protocolo Ethernet a uma interface gráfica baseada em Python (Sousa et al, 2018). Para validar a abordagem proposta adota-se a configuração não linear apresentada na Figura 5b, em que h 2 , h 3 e h 4 ∈ [0, 70] cm são as alturas da coluna de líquido nos tanques 2, 3 e 4 e B1 e B2 são as duas bombas hidráulicas do processo.…”

Dynamic controllers for local input‐to‐state stabilization of discrete‐time linear parameter‐varying systems with delay and saturating actuators

“…The following condition can be used to analyze the local stability of the LPV system (16) and, consequently, the local stability of (1)-(3) in closed-loop with a given parameter varying dynamic output feedback compensator (7).…”

“…then, i. for nonnull signals k ∈ , and for all 0 belonging to the set   0 =   ( ) ⊆  0 , with −1 = −1 − −1 , the trajectories of the system (16) do not leave the set   =   ( ) ⊆   , for all k > 0; ii. for k = 0, ∀ k > 0, the set   is a region of asymptotic stability for the LPV system (16).…”

“…14,15 It is worth to say that discrete-time models, which are the focus of this work, have an attractive issue because of their use to design and to implement digital controllers. 16,17 Another practical issue concerns the physical and safety limitations imposed by actuators, which can also lead the closed-loop system to degenerate performance and even to lose stability at some operational conditions. 18,19 In this case, the local stability analysis must be carried on, which requires the estimation of the region of initial conditions for which the trajectories converge asymptotically to the origin.…”

“…In this context, various methods to analyze the stability and to design parameter varying dynamic feedback controllers have been proposed for LPV continuous‐time systems 12,13 and for the discrete‐time counterparts 14,15 . It is worth to say that discrete‐time models, which are the focus of this work, have an attractive issue because of their use to design and to implement digital controllers 16,17 …”