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Hardware-in-the-loop (HIL) testing is part of automotive V-design which is commonly used in automotive industries for the development of Electronic Control Unit (ECU). HIL test platform provides the capacity to test the ECU in a controlled environment even with scenarios that would be too dangerous or impractical to test on real situation, also the ECU can be tested even before the actual plant under building. This paper presents a HIL test platform for the validation of a seat ECU. The HIL platform can also be used for control and diagnostics algorithm development. The HIL test platform consists of three parts: a real time target machine (dSPACE SCALEXIO AutoBox), an ECU (Magna Seating M12 Module), and a signal conditioning unit (Load Box). The ECU produces the control commands to the real-time target machine through load box. The real time target machine hosts the plant model of the power seat which includes the kinematics and dynamics of the seat movements.

This paper addresses Hardware-In-the-Loop modeling and simulation for Diesel aftertreatment controls system development. Lean NOx Trap (LNT) based aftertreatment system is an efficient way to reduce NOx emission from diesel engines. From control system perspective, the main challenge in aftertreatment system is to predict temperature at various locations and estimate the stored NOx in LNT. Accurate estimation of temperatures and NOx stored in the LNT will result in an efficient system control with less fuel penalty while still maintaining the emission requirements. The optimization of the controls will prolong the lifespan of the system by avoiding overheating the catalysts, and slow the progressive process of component aging. Under real world conditions, it is quite difficult and costly to test the performance of a such complex controller by using only vehicle tests and engine cells.

Among various applications of the automotive engine and chassis controls, PWM driven actuators and valves are commonly used, such as an electronic throttle control in both gasoline and diesel engines, variable valve timing control, EGR control, proportional valve regulating pressures, and etc. Many of such valves are used under the conditions of uncertainties, disturbances, frictions, wear and tear, system aging, and sensor noises as well. Maintaining system functions with high performance under such tough environments and through out the full useful life cycle, is highly desirable but very challenging for control designs. Additional component issues such as electronic signal drifting, mechanical hysteresis, frictions will just add another level of difficulties. In this paper, we proposed a unique and practical control strategy which is relatively easy to implement, however still able to provide us with advantages in handling robustness.

This paper addresses feedforward tracking control for electronic throttles. A robust and accurate tracking control scheme based on the training of a Neural Network model and feedback term (PID) is developed. The Neural Network based term can be trained off-line. This feedfoward term serves as a mathematical model capable of describing Electronic Throttle dynamics over a wide range. We have shown that by adding the Neural Network based feedforward control to a common feedback control method, such as the gain-scheduled PID used in many ETC production controllers, that the tracking control performance criteria such as transient errors, steady state errors, response time and overshoot, are greatly improved. Experiments conducted on a production Electronic Throttle Body with a Motorola H-brigde driver IC have shown good results utilizing this approach.