Ivo Punčochář

Brief paper: Active fault detection and control: Unified formulation and optimal design

A new unified formulation of the active fault detection and control problem for discrete-time stochastic systems and its optimal solution are proposed. The problem formulation stems from the optimal stochastic control problem and includes important special cases: an active detector and controller, an active detector and input signal generator, and an active detector with a given input signal generator. The optimal solution is derived using the so-called closed loop information processing strategy. This strategy respects the influence of the current decision and/or input on the future behavior of the observed system, allows penalizing future wrong decisions, and improves the quality of fault detection. The proposed formulation and obtained solution also provide better understanding of the active fault detection and its relation to the optimal stochastic control. The results are illustrated in numerical examples.

Closed loop information processing strategy for optimal fault detection and control

Abstract A new unique general formulation and solution of the active fault detection problem for a discrete-time stochastic system are proposed. The optimal active detectors and the dual controller are designed by the appropriate criteria minimization using closed-loop information processing strategy. The considered formulation encompasses the following cases: active detection with an a priori given input signal generator, active detection, and finally dual control as active detection combined with control. The results are illustrated in the linear multiple models framework.

A feasible design of active detector and input signal generato

Abstract The paper deals with the design of active detector and input signal generator for stochastic discrete-time systems. The active detection problem is formulated for the jump Markov linear Gaussian model. Since the optimal solution is intractable, some simplifications and approximations are considered. Firstly, the problem of discrimination between several models and its optimal solution are obtained as a special case of the active detection problem for jump Markov linear Gaussian model. Then, two different approximations of the optimal solution are carried out to obtain feasible design techniques of the active detector and input signal generator. The first approximation is based on interchanging the minimization and expectation operators. It is shown that this approximation leads to a known approach that uses the Bayesian risk as design criterion. The second approximation is based on l -step lookahead policy and rollout algorithm. Both feasible design techniques are compared in the numerical example.

Approximate active fault detection and control

This paper deals with approximate active fault detection and control for nonlinear discrete-time stochastic systems over an infinite time horizon. Multiple model framework is used to represent fault-free and finitely many faulty models. An imperfect state information problem is reformulated using a hyper-state and dynamic programming is applied to solve the problem numerically. The proposed active fault detector and controller is illustrated in a numerical example of an air handling unit.

Active fault detection and dual control in multiple model framework

Abstract The paper deals with active fault detection and dual control in the multiple model framework. A monitored and controlled system is described by a discrete-time linear stochastic model at each step of a finite time horizon. The model belongs to an a priori given set of models, and known transient probabilities describe switching between the models. The goal is to design an active detector and controller that processes all available information and generates decisions and inputs. The decisions inform whether a fault has occurred in the system, and the inputs should simultaneously control and excite the system. As the control is in conflict with the excitation, the dual control problem arises. It is shown that both active fault detection and dual control can be solved using Bellmans principle of optimality, and a corresponding backward recursive equation is derived. The approximative solution of the backward recursive equation is discussed, and an algorithm based on an application of rolling horizon and nonlinear filtering techniques is presented. The presented approach is illustrated in a simple numerical example.

Active fault detection: A comparison of probabilistic methods

The paper deals with probabilistic methods for designing the active fault detectors that improve the quality of detection using an auxiliary input signal. Two probabilistic methods that assume a similar stochastic model of a monitored system are considered and compared with a special attention to various difficulties associated with active fault detector designs. The active fault detector design based on a general detection cost function is compared with the model sequence selection error minimization design in terms of assumptions and theoretical properties. Practical aspects of both methods are also considered and demonstrated through a numerical example.

12th European Workshop on Advanced Control and Diagnosis (ACD 2015)

The 12th European Workshop on Advanced Control and Diagnosis (ACD 2015) took place at the Research Centre NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic, on November 19 - 20, 2015. The annual European Workshop on Advanced Control and Diagnosis has been organized since 2003 by Control Engineering departments of several European universities in Germany, France, the UK, Poland, Italy, Hungary, and Denmark to bring together senior and junior academics and engineers from diverse fields of automatic control, fault detection, and signal processing. The workshop provides an opportunity for researchers and developers to present their recent theoretical developments, practical applications, or even open problems. It also offers a great opportunity for industrial partners to express their needs and priorities and to review the current activities in the fields. A total of 74 papers have been submitted for ACD 2015. Based on the peer reviews 48 papers were accepted for the oral presentation and 10 papers for the poster presentation. The accepted papers covered areas of control theory and applications, identification, estimation, signal processing, and fault detection. In addition, four excellent plenary lectures were delivered by Prof. Fredrik Gustafsson (Automotive Sensor Mining for Tire Pressure Monitoring), Prof. Vladimir Havlena (Advanced Process Control for Energy Efficiency), Prof. Silvio Simani (Advanced Issues on Wind Turbine Modelling and Control), and Prof. Robert Babuska (Learning Control in Robotics). The ACD 2015 was for the first time in the workshop history co-sponsored by the International Federation of Automatic Control (IFAC). On behalf of the ACD 2015 organising committee, we would like to thank all those who prepared and submitted papers, participated in the peer review process, supported, and attended the workshop.

Nonlinear analysis of position estimate in global navigation satellite systems

The paper presents a nonlinear analysis of position estimation based on a global navigation satellite system. A classical problem formulation and iterative solution that results in the weighted least squares estimate of the receiver state are assumed. The analysis employs the Taylors theorem to express the nonlinear measurement model using the first order Taylor polynomial at the state estimate and the Lagrange form of the remainder. A sensitivity analysis of the Jacobian matrix pseudoinverse is performed, and an upper bound on the size of the Lagrange remainder is derived using eigenstructure of the Hessian matrix. The results obtained show that both the sensitivity of the pseudoinverse and the size of the quadratic term are not significant, and thus the linear approximation commonly used to derive stochastic properties of the state estimate is reasonable. Although this result has been experimentally confirmed by numerous successful applications, this analysis can serve as a more rigorous basis when the design procedures for a safety critical system have to be satisfied.

Infinite Horizon Input Signal for Active Fault Detection in Controlled Markov Chains

The paper deals with the problem of active fault detection with a given fault detector over an infinite time horizon. Systems that can be modeled using two interconnected discrete-time finite-state Markov chains are considered. The first Markov chain is unobservable and describes switching between fault-free and faulty modes. The second one is an observable controlled Markov chain that describes the system dynamics in fault-free and faulty modes. The maximum a posteriori probability fault detector is assumed, and an input signal generator that improves the decision quality is designed. The original problem is reformulated as a perfect state information problem and solved by dynamic programming. An infinite time horizon is considered to reduce off-line computational demands and a perceptron neural network is employed to lower memory requirements for on-line use. The results are illustrated in a numerical example.

Active Fault Detection and Constrained Control of Air Handling Unit

Abstract The paper deals with active fault detection and control for stochastic systems. An input signal generator for a given detector is designed such that detection and control aims are pursued. The detection aim is to minimize the probability of a wrong decision at the end of a finite horizon, and the control aim is to maintain the price of control actions below a given limit and satisfy some other design requirements. Since the control aim and other design requirements are enforced as constraints a constrained optimization problem is solved using the open loop information processing strategy. The proposed approach is applied to active detection of the stuck air mixing damper of an air handling unit.