Paolo Varutti

Event-based model predictive control for Networked Control Systems

Thanks to their cheap startup costs, flexibility, and standard infrastructure re-usability, Networked Control Systems have gained the attention of both the control community and the industry. Unfortunately, the presence of communication networks might introduce nondeterminism due to (random) delays and/or (unpredictable) information losses. In this paper, an event-based model predictive control approach for nonlinear continuous time systems under state and input constraints is presented. This method is able to counteract bounded delays, information losses, as well as deal with event triggering due to sensors and actuators. Under standard weak assumptions, closed loop stability, in the sense of asymptotic convergence, is achieved. Simulation results for a planar vertical takeoff and landing aircraft are provided.

Stabilizing Nonlinear Predictive Control over Nondeterministic Communication Networks

Networked control systems are systems in which distributed controllers, sensors, actuators and plants are connected via a shared communication network. The use of nondeterministic networks introduces two major issues: communication delays and packet dropouts. These problems cannot be avoided and they might lead to a degradation in performance, or, even worse, to instability of the system. Thus, it is important to take network effects directly into account. In this paper, nonlinear continuous time networked control systems are considered and a nonlinear model predictive controller that is able to compensate the network nondeterminism is outlined.

Robustness of Prediction Based Delay Compensation for Nonlinear Systems

Abstract Control of systems where the information between the controller, actuator, and sensor can be lost or delayed can be challenging with respect to stability and performance. One way to overcome the resulting problems is the use of prediction based compensation schemes. Instead of a single input, a sequence of (predicted) future controls is submitted and implemented at the actuator. If suitable, so-called prediction consistent compensation and control schemes, such as certain predictive control approaches, are used, stability of the closed loop in the presence of delays and packet losses can be guaranteed. In this paper, we show that control schemes employing prediction based delay compensation approaches do posses inherent robustness properties. Specifically, if the nominal closed loop system without delay compensation is ISS with respect to perturbation and measurement errors, then the closed loop system employing prediction based delay compensation techniques is robustly stable. We analyze the influence of the prediction horizon on the robustness gains and illustrate the results in simulation.

On the synchronization problem for the stabilization of networked control systems over nondeterministic networks

In recent years, networked control systems have gained the attention of the control community, since they allow to re-use the preexisting infrastructure therefore reducing deployment time and costs. Unfortunately, they also introduce new control challenges due to the nondeterministic network behavior. Predictive and model-based approaches can be used to compensate both delays and packet dropouts. However, a common timeframe among the involved components -sensors, actuators, plants and controllers- is required. This brings normally the necessity of keeping inner-clocks synchronized, which at the current state of art can be hard to realize. In this paper, a possible solution for the synchronization problem is presented. The idea is to keep using predictive techniques but utilizing a unique inner-clock on the system side. Assuming that actuator and sensor are directly connected to the system, the packets are time stamped, the delays are bounded, the maximum round-trip-time is known, and a limited amount of information is lost, it is possible to use model predictive control to stabilize the closed loop system by compensating delays and packet dropouts.

Event-based reduced-attention predictive control for nonlinear uncertain systems

Event-based control is an alternative to traditional control where new measurements are sampled only if critical events occur. This not only allows to reduce the control effort but it satisfies nowadays application requirements, such for example reduction of information exchange, computational power, or energy consumption. The work in this field is, however, still sparse and only a few results are available. Properly choosing an event-detection logic can considerably improve the overall systems performance. We propose a control algorithm which makes use of a model-based triggering strategy to reduce the control effort (reduced-attention control), while guaranteeing robustness against bounded additive perturbations for nonlinear continuous time systems. In particular, we derive conditions which guarantee that asymptotic stability of the nominal system implies practical stability of the real one in a neighborhood of the origin. A continuous stirred tank reactor is used as a benchmark problem to show the effectiveness of the presented algorithm.

Event-based NMPC for networked control systems over UDP-like communication channels

Networked controlled systems have recently received attention from the industry since they allow for flexibility and cost reduction. However, due to the fact that communication media can be subject to random delays, packet dropouts, jitters and other uncertainties, destabilization of the closed loop system can occur. Model predictive control has demonstrated to be a valid solution to cope with these issues. On the other hand, it typically relies on TCP-like (or connection oriented) protocols, i.e. either the received or the lost information is acknowledged. In this work, we propose an event-based model predictive control algorithm for nonlinear continuous time systems subject to state and input constraints which is based on UDP-like communication. We show that without the use of any acknowledgment or error message we can derive a compensation algorithm, which used in combination with the controller, under mild conditions, guarantees closed loop stability. The solution is applied to a continuous stirred tank reactor where an exothermic irreversible reaction takes place. The simulations show the effectiveness of the presented algorithm.

Model Predictive Control for Gust Load Alleviation

For economical and ecological reasons aircraft are required to become more efficient by reducing fuel consumption and CO

Dissipativity-based Distributed Nonlinear Predictive Control for Cascaded Systems

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Predictive Control of Nonlinear Chemical Processes under Asynchronous Measurements and Controls

emissions. One way to achieve these objectives is to decrease the weight of the aircraft structure. The reduction is, however, limited by the structural requirement that the aircraft must be able to withstand the forces and stress induced by maneuvers, turbulence, and gusts. Reduction of the forces and stress, for example by suitable flight control schemes, allows lighter structures and thus more efficiency. In this paper we focus on the problem of alleviating gust loads at critical locations of the airframe. This is accomplished via a model predictive control approach that accounts for look ahead measurements of incoming gust disturbances via light detection and ranging (LIDAR) systems. Deflecting control surfaces are used by the predictive controller to reduce the load effects caused by the disturbance, the aircraft rigid body, and the structural response. The proposed controller design takes actuator deflection- and rate-limitations, the complex multiple-inputs multiple-outputs system structure, and the objective to reduce critical loads directly into account. Predictive control proved itself to be an effective control strategy for gust load alleviation.

On the effect of enforcing stability in model predictive control for gust load alleviation

Abstract Developing centralized controllers for large-scale systems, e.g. complex chemical processes, electrical power networks, is an important, yet challenging problem. Typically, it would be of advantage if one could divide the problem into smaller parts (or subproblems), and to opt for a decentralized or distributed alternative. On the other hand, due to physical interconnections between subsystems, it is often complicated to find such a solution. Additionally, performance loss or even instability of the overall system can arise if the decentralization is not done properly. It is, therefore, fundamental to develop suitable distributed/decentralized techniques in order to avoid these issues. In this paper, we propose a decentralized dissipativity-based nonlinear model predictive control strategy for cascades of physically interconnected systems, e.g cascades of hydroelectric power plants, multi-cell batteries. Under mild assumptions, we prove that the control method guarantees overall stability by independently enforcing stability of the local subsystems. This allows for efficient decentralized solutions. Furthermore, the local controllers only exchange limited information and no model of the neighbor systems is required, facilitating decoupled local controller design. Two interconnected water tanks are used to demonstrate the effectiveness of our approach.