Under the condition that state variables are unavailable, a robust predictive fault-tolerant switching control method is proposed for discrete linear systems with actuator random failures. The randomness of failures is described in terms of conditional probabilities. Then, a transfer probability matrix is established to predict the probability of state change. A model incorporating the system state deviation and tracking error is further developed to enhance the regulation of the controller. By using related theories, sufficient conditions for the stability of the system are obtained. Furthermore, average dwell times for the system are obtained according to exponential stability analysis. These times and the matrix form conditions for the output feedback fault-tolerant switching controller to switch between conventional control and fault-tolerant control. Based on the previous work, the research on disturbances and set-point changes has been extended. The feasibility of the method is verified by a case study of the pressure-holding phase. 

Robust predictive fault-tolerant switching control for discrete linear systems with actuator random failures

Smart contracts (SC) are a key component in the Blockchain. Smart contracts are normal computer programs that are written mostly in solidity object-oriented programming language. Blockchain allows the completion of transactions only if the transaction obeying the smart contract rules. SC is not modifiable once they are deployed into the Blockchain. Thus it is essential to verify the quality of the smart contract before deploying it into the Blockchain. In this chapter, Bayesian Network Model is designed and constructed to measure the SC quality. The benefit of using a Bayesian network is that in addition to getting the probability of each quality measure, it will allow us to recommend the causes for the outcome of each quality measure. The proposed model with Bayesian Network helps in assessing the quality of smart contracts and the success rate is 82%. 

Chapter Six - Bayesian network-based quality assessment of blockchain smart contracts

Ethereum is one of the popular Blockchain platform. The key component in the Ethereum Blockchain is the smart contract. Smart contracts (SC) are like normal computer programs which are written mostly in solidity high-level object-oriented programming language. Smart contracts allow completing transactions directly between two parties in the network without any middle man or mediator. Modification of the smart contracts are not possible once deployed into the Blockchain. Thus smart contract has to be vulnerable free before deploying into the Blockchain. In this paper, Bayesian Network Model was designed and constructed based on Bayesian learning concept to detect smart contract security vulnerabilities which are Reentrancy, Tx.origin and DOS. The results showed that the proposed BNMC (Bayesian Network Model Construction) design is able to detect the severity of each vulnerability and also suggest the reasons for the vulnerability. The accuracy of the proposed BNMC results are improved (accuracy 8% increased for both Reentracy and Tx.origin, 6% increased for DOS), compared with traditional method LSTM. This proposed BNMS design and implementation is the first attempt to detect smart contract vulnerabilities using Bayesian Networks. 

Bayesian network based vulnerability detection of blockchain smart contracts

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation. 

Robust Predictive Fault-Tolerant Control under Actuator Random Failures

A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data

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A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data