A vehicle control system which can ensure high reliability, real-time processing, and expandability with a simplified ECU configuration and a low cost by backing up an error through coordination in the entire system without increasing a degree of redundancy of individual controllers beyond the least necessary level. The vehicle control system comprises a sensor controller for taking in sensor signals indicating a status variable of a vehicle and an operation amount applied from a driver, a command controller for generating a control target value based on the sensor signals taken in by the sensor controller, and an actuator controller for receiving the control target value from the command controller and operating an actuator to control the vehicle, those three controller being interconnected via a network. The actuator controller includes a control target value generating unit for generating a control target value based on the sensor signals taken in by the sensor controller and received by the actuator controller via the network when the control target value generated by the command controller is abnormal, and controls the actuator in accordance with the control target value generated by the control target value generating unit.

Vehicle Control System

A new method of seamless land navigation for GPS/INS integrated system

The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage.

Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system

In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages.

INS/GNSS integration using recurrent fuzzy wavelet neural networks

Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost low-precision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements.

A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments

Presently, Kalman filter (KF) is used to fuse data from both inertial navigation systems (INS) and global positioning systems (GPS) to provide position, velocity and attitude information. However, several drawbacks associated with KF like its immunity to noise, its dependency on predefined errors models, has encouraged research activates towards investigation of other integration techniques. This study proposes and discusses the real-time implementation of adaptive neuro-fuzzy inference system (ANFIS) to fuse GPS and INS data for vehicular navigation applications. The proposed method was examined and compared to KF when applied to Ashtech Z12 GPS receiver and a navigation-grade INS (Honeywell LRF-III) that have been utilized inside a land vehicle. The system is evaluated while considering several intentionally introduced GPS outages for periods of 20 seconds. The ANFIS-based navigation system was able to provide the vehicle position with errors, which were mostly below 2 m. The experimental results demonstrated the advantages of the proposed AI-based INS/GPS integration techniques in regards of robustness while ensuring system position accuracy in real-time.

Ahmed El Shafie

Papers

ANFIS-Based Model for Real-time INS/GPS Data Fusion for Vehicular Navigation System