Multivariable predictive PID control for quadruple tank

In this paper multivariable predictive PID controller has been implemented on a multi-inputs multi-outputs control problem i.e., quadruple tank system, in comparison with a simple multiloop PI controller. One of the salient feature of this system is an adjustable transmission zero which can be adjust to operate in both minimum and non-minimum phase configuration, through the flow distribution to upper and lower tanks in quadruple tank system. Stability and performance analysis has also been carried out for this highly interactive two input two output system, both in minimum and non-minimum phases. Simulations of control system revealed that better performance are obtained in predictive PID design

Detection of replay attacks in cyber-physical systems using a frequency-based signature

This paper proposes a frequency-based approach for the detection of replay attacks affecting cyber-physical systems (CPS). In particular, the method employs a sinusoidal signal with a time-varying frequency (authentication signal) into the closed-loop system and checks whether the time profile of the frequency components in the output signal are compatible with the authentication signal or not. In order to carry out this target, the couplings between inputs and outputs are eliminated using a dynamic decoupling technique based on vector fitting. In this way, a signature introduced on a specific input channel will affect only the output that is selected to be associated with that input, which is a property that can be exploited to determine which channels are being affected. A bank of band-pass filters is used to generate signals whose energies can be compared to reconstruct an estimation of the time-varying frequency profile. By matching the known frequency profile with its estimation, the detector can provide the information about whether a replay attack is being carried out or not. The design of the signal generator and the detector are thoroughly discussed, and an example based on a quadruple-tank process is used to show the application and effectiveness of the proposed method.Peer ReviewedPostprint (author's final draft

Decentralized event-triggered control over wireless sensor/actuator networks

In recent years we have witnessed a move of the major industrial automation providers into the wireless domain. While most of these companies already offer wireless products for measurement and monitoring purposes, the ultimate goal is to be able to close feedback loops over wireless networks interconnecting sensors, computation devices, and actuators. In this paper we present a decentralized event-triggered implementation, over sensor/actuator networks, of centralized nonlinear controllers. Event-triggered control has been recently proposed as an alternative to the more traditional periodic execution of control tasks. In a typical event-triggered implementation, the control signals are kept constant until the violation of a condition on the state of the plant triggers the re-computation of the control signals. The possibility of reducing the number of re-computations, and thus of transmissions, while guaranteeing desired levels of performance makes event-triggered control very appealing in the context of sensor/actuator networks. In these systems the communication network is a shared resource and event-triggered implementations of control laws offer a flexible way to reduce network utilization. Moreover reducing the number of times that a feedback control law is executed implies a reduction in transmissions and thus a reduction in energy expenditures of battery powered wireless sensor nodes.Comment: 13 pages, 3 figures, journal submissio

Real Time Model Predictive Control in JModelica.org

In this thesis a framework for real-time model predictive control has been developed for JModelica.org, which is an open-source platform for simulation and analysis of dynamical systems. Model predictive control (MPC) is an advanced optimizationbased control method that uses a model of the process being controlled to optimize control. The framework was tested on three different processes, real and simulated, and its performance was compared with that of an linear-quadratic regulator (LQR), which is a simpler type of controller that uses multiplication with a pre-calculated matrix to calculate the control signal from the state vector. The MPC controller was found to perform as well as or better than the LQR controller in all cases, with the main improvements being seen in the MPC controller’s ability to handle process constraints or when far from the LQR controller’s linearization point; however, the LQR controller was much faster in calculating the control signal. This also served as a first test of using JModelica.org to perform MPC on real processes, and although it performed well on the two it was tested on, further work will be needed if the MPC framework should be able to handle processes that are much faster or more complex

The four-tank benchmark: a simple solution by embedded model control

The four-tank benchmark is a multivariate and nonlinear control problem which has been widely studied in the literature. Two pairs of tanks in series are supplied by two pumps. Under certain configurations, the Embedded Model Control approach provides a simple decoupled solution by separately controlling the two output tank levels and treating the input flow as a partly unknown disturbance. Neglected dynamics in a form of unknown delays both in sensors and actuator dynamics is considered. The core of the control unit is a discrete-time embedded model consisting of unknown disturbance dynamics and partly known nonlinear interactions. The embedded model is driven by the plant command and by a feedback vector which is retrieved from the model error. The feedback is capable of keeping updated the unknown disturbance prediction, ready to be cancelled by the control law. The control gains are tuned using two sets of closed-loop eigenvalues in order to trade-off between disturbance rejection and robust stability. Simulated runs under different tank interactions prove design effectiveness

Model-based control algorithms for the quadruple tank system: An experimental comparison

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad's IMC tuning rules using a transfer function representation of the linearized model. Norms of the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance.Comment: 6 pages, 5 figures, 3 tables, to be published in Foundations of Computer Aided Process Operations / Chemical Process Control (FOCAPO/CPC 2023). Hilton San Antonio Hill Country, San Antonio, Texa

A communication-based distributed model predictive control approach for large-scale systems

This work presents a distributed model predictive control strategy as an alternative to conventional centralized approaches, which often suffer from implementation issues when applied to large-scale systems. The overall system is partitioned into minimally coupled subsystems based on its structural properties. Then, the coordination among the subproblems is achieved by means of a communication protocol, which allows each local controller to broadcast its solution to the rest of controllers with a coupled variable. The proposed approach is tested on the quadruple-tank process, and its effectiveness is proved by comparing the obtained results to those documented in an existing benchmark.Peer ReviewedPostprint (author's final draft