This paper presents a robust Fault-Tolerant Control (FTC) methodology for the design of virtual sensors and virtual actuators for discrete-time Linear Parameter Varying (LPV) systems. Conditions based on Linear Matrix Inequalities (LMIs) are proposed for the synthesis of a reconfiguration block composed of a virtual actuator and a virtual sensor, guaranteeing input-to-state stability (ISS). The main contribution of the proposed FTC approach is to deal with LPV models where input and output matrices can be parameter-dependent. Moreover, differently from results found in the literature, a single reconfiguration block can be designed to be robust to different kinds and magnitudes of faults. Real-time level-control experiments illustrate the efficiency of the proposed procedure; the system used in the experiments consists of a nonlinear two coupled tanks with two inputs and two outputs, represented by an LPV model subject to sensor and actuator faults. Experimental results and comparisons with results in the literature indicate that the proposed approach is able to mitigate the fault effects with better performance indices than the literature approaches.

Fault tolerant control for linear parameter varying systems: An improved robust virtual actuator and sensor approach.

This paper deals with piecewise constant set-points tracking control of nonlinear discrete-time systems represented by Takagi-Sugeno models under actuators’ saturation. To this end, a fuzzy Proportional Integral-like (PI-like) discrete-time control scheme is considered, which consists of a proportional state feedback, an integral action over the tracking error, and an anti-windup action. All the control gains are obtained through a convex optimization procedure formulated in term of Linear Matrix Inequalities (LMIs). The proposed method yields a Parameter Distributed Compensation (PDC) PI-like control and a non-PDC anti-windup action structure. Due to the actuators’ saturation, a local approach is considered with a fuzzy Lyapunov function to ensure the local closed-loop stability, to provide an estimate of the region of attraction, and to compute the amplitude bounds of set-points changes. This latter issue allows delivering operational security by providing a bounded range for the set-points variation. To validate and illustrate the performance of the proposed tracking control approach, real-time experiments has been performed on an industrial oriented process consisting on the nonlinear level control of two interactive tanks.

Anti-windup TS Fuzzy PI-like Control for Discrete-Time Nonlinear Systems with Saturated Actuators

A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid

Design of LPV-PI-like controller with guaranteed performance for discrete-time systems under saturating actuators

In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control—a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant.

/pdf/bridging-the-gap-in-technology-transfer-for-advanced-process-3apozeo4.pdf

Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications

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.

/pdf/affordable-control-platform-with-mpc-application-4bjlz73xd1.pdf

Affordable Control Platform with MPC Application

The command governor (CG) is a useful strategy to avoid constraints violation in case of control reconfiguration or operational conditions changes, like switched systems mode changes. In general, the structure of the CG specializes in producing an alternative reference signal to drive a closed-loop system to the desired reference while avoiding constraints violations in the control signal, state-space, or combinations of them. In this paper, we propose a simple strategy that modifies the conventional structures of the CG and supervisor, yielding a switched rule based on the estimated controllers' region of attraction. We present some simulations to illustrate the proposal's potential and compare it with a competitor scheme exploiting a dwell-time approach. The results suggest that our approach adds new possibilities of CG and supervisor design, reducing the transition time between system modes and improving the closed-loop performance indexes.

Command Governor strategy based on region of attraction control switching

http://manuscript.elsevier.com/S1874548222000099/pdf/S1874548222000099.pdf

Load-altering attack detection on smart grid using functional observers

The interconnectedness of the CPS allows sharing data with relevant systems and taking more informed process control decisions; however, it also broadens the attack surface. One pain point of IoT, technology usually applied in CPS, is that embedded devices are usually low energy and do not have enough processing power to operate while keeping up with security measures, such as encryption and data validation. As a result, attackers may use such devices to launch attacks on the network, compromising the whole CPS infrastructure. One common type of attack is the False Data Injection (FDI), in which the attacker has access to a communication channel and can change the value read by a sensor or sent to an actuator. One way of coping with such attacks is to develop a bank of observer by using Functional Observers, which are better suited for high-dimensional, sparse systems. It does not suffer from sparsity\x92s numerical problems and uses a reduced-order system to observe only the desired states, significantly reducing the computational burden of the observer. We propose an LMI-based approach to design a bank of residual generators for functional observers to detect such attacks. This approach has the advantage of using a reduced order arbitrary dynamic system, making it suitable for large-scale smart grids, and the use of LMI, allowing the easy insertion of restrictions 

Álan C. E. Sousa

Papers

Affordable Control Platform with MPC Application

Command Governor strategy based on region of attraction control switching

Load-altering attack detection on smart grid using functional observers

False Data Injection Detection in Cyber-Physical System