Inferential networked control with accessibility constraints in both the sensor and actuator channels

The predictor and controller design for an inferential control scheme over a network is addressed. A linear plant with disturbances and measurement noise is assumed to be controlled by a controller that communicates with the sensors and the actuators through a constrained network. An algorithm is proposed such that the scarce available outputs are used to make a prediction of the system evolution with an observer that takes into account the amount of lost data between successful measurements transmissions. The state prediction is then used to calculate the control actions sent to the actuator. The possibility of control action drop due to network constraints is taken into account. This networked control scheme is analyzed and both the predictor and controller designs are addressed taking into account the disturbances, the measurement noise, the scarce availability of output samples and the scarce capability of control actions update. The time-varying sampling periods that result for the process inputs and outputs due to network constraints have been determined as a function of the probability of successful transmission on a specified time with a Bernoulli distribution. For both designs H∞ performance has been established and LMI design techniques have been used to achieve a numerical solution

Jump state estimation with multiple sensors with packet dropping and delaying channels

This work addresses the design of a state observer for systems whose outputs are measured through a communication network. The measurements from each sensor node are assumed to arrive randomly, scarcely and with a time-varying delay. The proposed model of the plant and the network measurement scenarios cover the cases of multiple sensors, out-of-sequence measurements, buffered measurements on a single packet and multirate sensor measurements. A jump observer is proposed that selects a different gain depending on the number of periods elapsed between successfully received measurements and on the available data. A finite set of gains is pre-calculated offline with a tractable optimisation problem, where the complexity of the observer implementation is a design parameter. The computational cost of the observer implementation is much lower than in the Kalman filter, whilst the performance is similar. Several examples illustrate the observer design for different measurement scenarios and observer complexity and show the achievable performance

Performance Tradeoffs for Networked Jump Observer-Based Fault Diagnosis

Print Request Permissions In this paper, we address the fault diagnosis problem for discrete-time multi-sensor systems over communication networks with measurement dropouts. We use the measurement outcomes to model the measurement reception scenarios. Based on this, we propose the use of a jump observer to diagnose multiple faults. We model the faults as slow time-varying signals and introduce this dynamic in the observer to estimate the faults and to generate a residual. The fault detection is assured by comparing the residual signal with a prescribed threshold. We design the jump observer, the residual and the threshold to attain disturbance attenuation, fault tracking and detection conditions and a given false alarm rate. The false alarm rate is upper bounded by means of Markov's inequality. We explore the tradeoffs between the minimum detectable faults, the false alarm rate and the response time to faults of the fault diagnoser. By imposing the disturbances and measurement noises to be Gaussian, we tighten the false alarm rate bound which improves the time needed to detect a fault. A numerical example is provided to illustrate the effectiveness of the theory developed in the paper

Co-design of jump estimators and transmission policies for wireless multi-hop networks with fading channels

We study transmission power budget minimization of battery-powered nodes in a remote state estimation problem over multi-hop wireless networks. Communication links between nodes are subject to fading, thereby generating random dropouts. Relay nodes help to transmit measurements from distributed sensors to an estimator node. Hopping through each relay node introduces a unit delay. Motivated by the need for estimators with low computational and implementation cost, we propose a jump estimator whose modes depend on a Markovian parameter that describes measurement transmission outcomes over a finite interval. It is well known that transmission power helps to increase the reliability of measurement transmissions, at the expense of reducing the life-time of the nodes’ battery. Motivated by this, we derive a tractable iterative procedure, based on semi-definite programming, to design a finite set of filter gains, and associated power control laws to minimize the energy budget while guaranteeing an estimation performance level. This procedure allows us to tradeoff the complexity of the filter implementation with performance and energy use.This work has been funded by projects TEC2015-69155-R from MICINN, PI15734, E-2015-15 and P1⋅1B2015-42 from Universitat Jaume I. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Hideaki Ishii under the direction of Editor Christos G. Cassandras

Estimation and fault diagnosis strategies for networked control systems

Communication networks increase flexibility of industrial monitoring, supervisory and control systems. However, they introduce delays or even dropouts on the transmitted information that affect the performance and robustness on the decision and control mechanisms in the system. This thesis contributes theoretically to the state estimation and fault diagnosis problem over networks. First, we study the state estimation problem. Motivated by reducing the implementation computational load of Luenberger-type estimators, we focus on predefined gain approaches for different network transmission conditions. In general, we propose jump estimators whose gains are related to the different network-induced data reception scenarios. We define the estimator complexity in terms of the number of different stored gains. Considering constant successful transmission probabilities, our main contribution here is the design of jump linear estimators to attain favorable trade-offs between estimation performance and estimator complexity. We show that one can reduce the estimator complexity while guaranteeing a similar performance than the optimal Kalman Filter. When dropouts are governed by a non-stationary stochastic process, the successful transmission probability is time-varying and may be unknown. For this case, we propose an estimator whose gains are scheduled in real-time with rational functions of the estimated packet arrival rate. We turn the design procedure into an optimization problem over polynomials that is numerically solved employing sum-of-squares (SOS) decomposition techniques. Second, motivated by reducing the network resource consumption without considerably degrading the estimation performance, we study the jointly design of jump linear estimators and predefined network operation conditions (co-design) to guarantee a favorable trade-off. Focusing on wireless networks with self-powered nodes, where transmitting is the most energy consuming task, we analyze two approaches for the network operation: event-based transmissions and power control. For the event-based approach, we use a Send-on-Delta protocol which reduces the number of transmissions with respect to transmitting at each sampling instant. However, it leads to an unknown successful transmission probability. For this framework, we contribute by characterizing this uncertainty and including it on the stochastic behavior of the estimator by means of a SOS-based design. Power control strategies are developed over a multi-hop wireless network with fading channels. Instead of reducing the number of transmission, power control acts directly on the transmission power. Higher transmission powers imply higher successful transmission probability values. Finally, motivated by the need of assuring a reliable operation of the networked system, we study the fault diagnosis problem. We explore and point out the trade-offs between fast fault detection and fault tracking conditions. We design jump estimatorbased fault diagnosers in which we can specify the minimum detectable faults, false alarm rate and response time to faults. Another contribution is a tightened version of existing false alarm rate bounds. Moreover, we also address the case when the control input is transmitted through a network without delivery acknowledgement. In this case, we improve fault diagnosisaccuracy by scheduling in real time the estimator jumping gains with rational functions that depend on a statistic of the control input error (difference between the control command being applied in the process and the one being used in the estimator). Again, we use a SOS-based solution approach to make the design problem tractable

Synthesis of nonlinear controller for wind turbines stability when providing grid support

This paper presents a new nonlinear polynomial controller for wind turbines that assures stability and maximizes the energy produced while imposing a bound in the generated power derivative in normal operation (guarantees a smooth operation against wind turbulence). The proposed controller structure also allows eventually producing a transient power increase to provide grid support, in response to a demand from a frequency controller. The controller design uses new optimization over polynomials techniques, leading to a tractable semidefinite programming problem. The ability of the wind turbine to increase its power under partial load operation has been analysed. The aforementioned optimization techniques have allowed quantifying the maximum transient overproduction that can be demanded to the wind turbine without violating minimum speed constraints (that could lead to unstable behaviour), as well as the total generated energy loss. The ability to evaluate this shortfall has permitted the development of an optimization procedure in which wind farm overproduction requirements are divided into individual turbines, assuring that the total energy loss in the wind farm is minimum, while complying with the maximum demanded power constraints. Copyright © 2013 John Wiley & Sons, Ltd

Performance-based design of PI observers for fault diagnosis in LTI systems under Gaussian noises

Ponencia presentada al 3rd Conference on Control and Fault-Tolerant Systems (SysTol), Barcelona, Spain, Sept. 7-9, 2016This work addresses the fault diagnosis problem for LTI systems under the presence of Gaussian noises through model-based proportional-integral observers with predefined gains. We propose an integrated design of residual generators and evaluators which takes into account the trade-off between physically meaningful parameters such as the false alarm rate, the minimum isolable faults and the integral squared error of the residuals under step faults. Dynamical fault isolation is also taken into account. In order to solve this design problem, we present two different approaches: one based on the steady-state Kalman filter and another based on convex optimization techniques.This work has been supported by grants FPU14/01592 from MECD and PI15734 from Universitat Jaume I, and by projects P11B2015-42 from Universitat Jaume I de Castellón o and MICINN project number TEC2015- 69155-R

Co-design of H∞ jump observers for event-based measurements over networks

This work presents a strategy to minimise the network usage and the energy consumption of wireless battery-powered sensors in the observer problem over networks. The sensor nodes implement a periodic send-on-delta approach, sending new measurements when a measure deviates considerably from the previous sent one. The estimator node implements a jump observer whose gains are computed offline and depend on the combination of available new measurements. We bound the estimator performance as a function of the sending policies and then state the design procedure of the observer under fixed sending thresholds as a semidefinite programming problem. We address this problem first in a deterministic way and, to reduce conservativeness, in a stochastic one after obtaining bounds on the probabilities of having new measurements and applying robust optimisation problem over the possible probabilities using sum of squares decomposition. We relate the network usage with the sending thresholds and propose an iterative procedure for the design of those thresholds, minimising the network usage while guaranteeing a prescribed estimation performance. Simulation results and experimental analysis show the validity of the proposal and the reduction of network resources that can be achieved with the stochastic approach