This paper presents the modeling and control of an opposed piston (OP) engine in a novel hybrid architecture. The OP engine was selected for this work due to the inherent thermody-namic benefits and the balanced nature of the engine. The typical geartrain required on an OP engine was exchanged for two electric motors, significantly reducing friction and decoupling the crankshafts. By using the motors to control the crankshaft motion profiles, this configuration introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To realize these opportunities, a model of the system capturing the instantaneous engine dynamics is essential along with methodology to regulate the crankshaft’s rotational dynamics utilizing the electric motors. The modeling presented here couples a 1D model capturing the gas exchange process during scavenging and a 0D model of the crankshaft dynamics and the heat release profile due to combustion. With the use of this model, a linear quadratic controller with reference feedforward was designed to track the crankshaft motion trajectory. Experimental results are used to validate the model and controller performance. These results highlight the sensitivity to model uncertainty at points with high cylinder pressure, leading to large differences in control input near minimum volume. The proposed controller is, however, still able to maintain tracking error for crankshaft position below ± 1 degree.
A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.
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