Recursive filtering for a class of nonlinear systems with missing measurements

This paper is concerned with the finite-horizon recursive filtering problem for a class of nonlinear time-varying systems with missing measurements. The missing measurements are modeled by a series of mutually independent random variables obeying Bernoulli distributions with possibly different occurrence probabilities. Attention is focused on the design of a recursive filter such that, for the missing measurements, an upper bound for the filtering error covariance is guaranteed and such an upper bound is subsequently minimized by properly designing the filter parameters at each sampling instant. The desired filter parameters are obtained by solving two Riccati-like difference equations that are of a recursive form suitable for online applications. A simulation example is exploited to demonstrate the effectiveness of the proposed filter design scheme. © 2012 IEEE.published_or_final_versio

Study on State Predictive Controllers for Networked Control System

When different control components of a closed loop control system are connected through a common network channel then the resulting control system is a Networked Control System. This spatially distributed system has several advantages like reduced system wiring, easy fault detection and maintenance capability.Unfortunately the introduction of communication channel results in several disadvantages like network induced delays and packet dropouts leading to loss of synchronism in the control system. The network induced imperfections causes system instability and complexity for the control engineers to design a suitable controller in order to compensate their effect on closed loop control system. In addition to the complexity in design the network induced imperfections should be measured, analysed by incorporating them in the closed loop control system. The project investigates the problem of network induced time delays in a networked control system by studying the behaviour of network induced time delay in a control system controlled by Linear Quadratic controller or a Pole placement controller using the states obtained from discrete Kalman filter state estimation, which estimates the current state in the presence of state and output noises. Further a control augmentation method is used by incorporating network induced delay in the plant model control vector. The time delayed control vector creates difficulty in designing the controller which is solved by time shifting approach. Further a state predictor is designed by using plant model transition matrix to predict the future states from present and past values of control vector and state estimate. Hence an optimal predictive controller is designed wherein the Linear Quadratic or pole placement controller uses the predictive state obtained from the state predictor to compensate the effect of network induced time delay and improve the control system performance

Decoding the `Nature Encoded\u27 Messages for Wireless Networked Control Systems

Because of low installation and reconfiguration cost wireless communication has been widely applied in networked control system (NCS). NCS is a control system which uses multi-purpose shared network as communication medium to connect spatially distributed components of control system including sensors, actuator, and controller. The integration of wireless communication in NCS is challenging due to channel unreliability such as fading, shadowing, interference, mobility and receiver thermal noise leading to packet corruption, packet dropout and packet transmission delay. In this dissertation, the study is focused on the design of wireless receiver in order to exploit the redundancy in the system state, which can be considered as a `nature encoding\u27 for the messages. Firstly, for systems with or without explicit channel coding, a decoding procedures based on Pearl\u27s Belief Propagation (BP), in a similar manner to Turbo processing in traditional data communication systems, is proposed to exploit the redundancy in the system state. Numerical simulations have demonstrated the validity of the proposed schemes, using a linear model of electric generator dynamic system. Secondly, we propose a quickest detection based scheme to detect error propagation, which may happen in the proposed decoding scheme when channel condition is bad. Then we combine this proposed error propagation detection scheme with the proposed BP based channel decoding and state estimation algorithm. The validity of the proposed schemes has been shown by numerical simulations. Finally, we propose to use MSE-based transfer chart to evaluate the performance of the proposed BP based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP based sequential and iterative channel decoding and state estimation. The numerical results show that MSE-based transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme. In this dissertation, the study is focused on the design of wireless receiver in order to exploit the redundancy in the system state, which can be considered as a `nature encoding\u27 for the messages. Firstly, for systems with or without explicit channel coding, a decoding procedures based on Pearl\u27s Belief Propagation (BP), in a similar manner to Turbo processing in traditional data communication systems, is proposed to exploit the redundancy in the system state. Numerical simulations have demonstrated the validity of the proposed schemes, using a linear model of electric generator dynamic system. Secondly, we propose a quickest detection based scheme to detect error propagation, which may happen in the proposed decoding scheme when channel condition is bad. Then we combine this proposed error propagation detection scheme with the proposed BP based channel decoding and state estimation algorithm. The validity of the proposed schemes has been shown by numerical simulations. Finally, we propose to use MSE-based transfer chart to evaluate the performance of the proposed BP based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP based sequential and iterative channel decoding and state estimation. The numerical results show that MSE-based transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme

State Estimation with Unconventional and Networked Measurements

This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive

State Estimation with Unconventional and Networked Measurements

This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive

Optimal Sequence-Based Control of Networked Linear Systems

In Networked Control Systems (NCS), components of a control loop are connected by data networks that may introduce time-varying delays and packet losses into the system, which can severly degrade control performance. Hence, this book presents the newly developed S-LQG (Sequence-Based Linear Quadratic Gaussian) controller that combines the sequence-based control method with the well-known LQG approach to stochastic optimal control in order to compensate for the network-induced effects