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Efficient cross-layer concurrent error detection in nonlinear control systems using mapped predictive check states

The rapid proliferation of sensor networks and robots in a wide range of societal applications has focused renewed attention on error-free operation of their underlying signal processing and control functions for reasons of safety and reliability. While real-time error detection in linear systems has been investigated in the past, error detection in nonlinear control functions has largely relied on implementing redundancy in components, units, or subsystems resulting in excessive area/performance overheads. In this paper, we introduce a realtime error detection methodology for nonlinear control state space systems that uses mapped predictive check states for detecting sensor and actuator malfunctions and transient errors in the execution of the control algorithm on the underlying processor. In our approach, the check state at time t bears a known relationship with the corresponding states of the nonlinear system. This check state can also be predicted from knowledge of the prior system states and inputs using nonlinear mappings. Consistency between the prior known relationship and its predicted value above, is used to check for errors in system function. We demonstrate the proposed approach on two test cases - a classical nonlinear inverted pendulum balancing problem using a moving cart and a nonlinear sliding mode controller driven electromagnetic brake-by-wire (BBW) system. Simulation results show the effectiveness of the proposed approach for detecting degradation of the sensor and actuator functions and soft errors in the execution of the control algorithms.