Mahdi Shahbakhti

Mohammad Reza Amini, Mahdi Shahbakhti, Jing Sun

This paper presents a new predictive second order sliding controller (PSSC) formulation for setpoint tracking of constrained linear systems. The PSSC scheme is developed by combining the concepts of model predictive control (MPC) and second order discrete sliding mode control. In order to guarantee the feasibility of the PSSC during setpoint changes, a virtual reference variable is added to the PSSC cost function to calculate the closest admissible set point. The states of the system are then driven asymptotically to this admissible setpoint by the control action of the PSSC. The performance of the proposed PSSC is evaluated for an advanced automotive engine case study, where a high fidelity physics-based model of a reactivity controlled compression ignition (RCCI) engine is utilized to serve as the virtual test-bed for the simulations. Considering the hard physical constraints on the RCCI engine states and control inputs, simultaneous tracking of engine load and optimal combustion phasing is a challenging objective to achieve. The simulation results of testing the proposed PSSC on the high fidelity RCCI model show that the developed predictive controller is able to track desired engine load and combustion phasing setpoints, with minimum steady state error, and no overshoot. Moreover, the simulation results confirm the robust tracking performance of the PSSC during transient operations, in the presence of engine cyclic variability.

Mohammad Reza Amini, Mahdi Shahbakhti

In this paper, the dynamic couplings among different inputs and outputs of a highly nonlinear spark ignition (SI) engine control problem during the cold start phase are evaluated by using relative gain array (RGA) technique. First, based on the experimental data, a multi-input multi-output model is developed to represent the engine dynamics. Second, the coupling among different inputs and outputs of the model is evaluated by using RGA technique in both open-loop and closed-loop structures. The results show that although there is an internal coupling within the engine dynamics in the open-loop framework, the closed-loop engine controller can be designed using a decentralized structure without significantly affecting the system performance. In the next step, based on a nonlinear physics-based model of the engine, a set of single-input single-output (SISO) adaptive second order discrete sliding mode controllers (DSMC) are designed to drive the states of the engine model to their pre-defined desired trajectories and minimize the tailpipe HC emission, under modelling and implementation (data sampling and quantization) uncertainties. The real-time test results on an actual engine control unit (ECU) show that the proposed SISO adaptive second order DSMC provides accurate and fast tracking performance for the highly nonlinear and internally coupled engine dynamics, and can meet the HC emission limit by controlling the engine-out emissions and exhaust catalytic converter efficiency.

Amirreza Yasami, Mohammadali Tofigh, Mahdi Shahbakhti et al.

Accurate driving cycle construction is crucial for vehicle design, fuel economy analysis, and environmental impact assessments. A generative Physics-Informed Expected SARSA-Monte Carlo (PIESMC) approach that constructs representative driving cycles by capturing transient dynamics, acceleration, deceleration, idling, and road grade transitions while ensuring model fidelity is introduced. Leveraging a physics-informed reinforcement learning framework with Monte Carlo sampling, PIESMC delivers efficient cycle construction with reduced computational cost. Experimental evaluations on two real-world datasets demonstrate that PIESMC replicates key kinematic and energy metrics, achieving up to a 57.3% reduction in cumulative kinematic fragment errors compared to the Micro-trip-based (MTB) method and a 10.5% reduction relative to the Markov-chain-based (MCB) method. Moreover, it is nearly an order of magnitude faster than conventional techniques. Analyses of vehicle-specific power distributions and wavelet-transformed frequency content further confirm its ability to reproduce experimental central tendencies and variability.