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In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separation technique is employed to overcome the design difficulty caused by the nonstrict-feedback structure. The most outstanding novelty of this paper is that individual Lyapunov function of each subsystem is constructed by flexibly adopting the upper and lower bounds of the control gain functions of each subsystem. Furthermore, by combining the average dwell-time scheme and the adaptive backstepping design, a valid adaptive neural state-feedback controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. Finally, the availability of the developed control scheme is verified by two simulation examples.

In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called "explosion of complexity" problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions. The designed controller ensures that all the closed-loop variables are ultimately boundedness, while the output limit is not transgressed and the output tracking error can be reduced arbitrarily small. Furthermore, the use of an asymmetric BLF is also explored to handle the case of asymmetric output constraint as a generalization result. Finally, the control performance of the presented control schemes is illustrated via two examples.

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Control-Flow Integrity (CFI) is a software-hardening technique. It inlines checks into a program so that its execution always follows a predetermined Control-Flow Graph (CFG). As a result, CFI is effective at preventing control-flow hijacking attacks. However, past fine-grained CFI implementations do not support separate compilation, which hinders its adoption.We present Modular Control-Flow Integrity (MCFI), a new CFI technique that supports separate compilation. MCFI allows modules to be independently instrumented and linked statically or dynamically. The combined module enforces a CFG that is a combination of the individual modules' CFGs. One challenge in supporting dynamic linking in multithreaded code is how to ensure a safe transition from the old CFG to the new CFG when libraries are dynamically linked. The key technique we use is to have the CFG represented in a runtime data structure and have reads and updates of the data structure wrapped in transactions to ensure thread safety. Our evaluation on SPECCPU2006 benchmarks shows that MCFI supports separate compilation, incurs low overhead of around 5%, and enhances security.

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