Yuhu Wu

Policy Iteration Approach to Control Residual Gas Fraction in IC Engines Under the Framework of Stochastic Logical Dynamics

This brief investigates the cycle-to-cycle transient behavior of the residual gas fraction (RGF) in terms of systems theory and proposes a multivalued logic-based optimal control strategy for the attenuation of RGF fluctuation. First, an in-cylinder pressure-based method for measuring RGF is provided, and the stochastic properties of the RGF are examined based on statistical data obtained by conducting experiments on a full-scale internal combustion engine test bench. Based on the observation of the experiment, a stochastic logical transient model is proposed to represent the cycle-to-cycle transient behavior of the RGF. Then, an optimal feedback control law, which targets the rejection of the RGF fluctuation, is derived using the policy iteration algorithm. Finally, the experimental result is demonstrated to show the effectiveness of the proposed optimal control law.

Experimental comparisons of hypothesis test and moving average based combustion phase controllers

For engine control, combustion phase is the most effective and direct parameter to improve fuel efficiency. In this paper, the statistical control strategy based on hypothesis test criterion is discussed. Taking location of peak pressure (LPP) as combustion phase indicator, the statistical model of LPP is first proposed, and then the controller design method is discussed on the basis of both Z and T tests. For comparison, moving average based control strategy is also presented and implemented in this study. The experiments on a spark ignition gasoline engine at various operating conditions show that the hypothesis test based controller is able to regulate LPP close to set point while maintaining the rapid transient response, and the variance of LPP is also well constrained.

Policy Iteration Algorithm for Optimal Control of Stochastic Logical Dynamical Systems

This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control problem is presented in an algebraic form. Then, employing the method of semitensor product of matrices and the increasing-dimension technique, a succinct algebraic form of the policy iteration algorithm is derived to solve the optimal control problem. To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated.

Reach Control Problem for Linear Differential Inclusion Systems on Simplices

This technical note concerns the reach control problem for the dynamical systems represented by linear differential inclusions. The goal is to derive conditions for the trajectories of a differential inclusion defined on a full-dimensional simplex to reach exit facets in finite time using affine feedback. To achieve this goal, an invariance condition is firstly derived for Lipschitz differential inclusions. As an application of the obtained invariance condition, the reach control problem (RCP) is solved for two classes of differential inclusions: norm-bounded linear differential inclusion and polytypic linear differential inclusion, in the sense of strong and weak reachability, respectively.

Challenges and solutions in automotive powertrain systems

Automotive powertrain mainly consisting of combustion engine, motor and battery (i.e. special for hybrid powertrain) is a very complicated integration system, and the research on the automotive pow...

Observability of Boolean multiplex control networks

Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results.