Application Of Optimal Control Methods Research Articles

Integrated Predictive Powertrain Control for a Multimode Plug-in Hybrid Electric Vehicle

Due to the complexity of a multimode plug-in hybrid electric vehicle (PHEV) powertrain, the energy management strategy of the said powertrain is a prime candidate for the application of optimal control methods. This article presents a predictive control strategy for optimal mode selection and powertrain control for a multimode PHEV capable of real-time control. This method utilizes predictions of future vehicle behavior in order to plan an optimal path of vehicle powertrain modes that minimizes energy consumption. This article also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using nonlinear model predictive control to create a real-time integrated predictive powertrain controller (IPPC) responsible for all aspects of multimode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multimode HEV powertrain control: an integrated discrete and continuous optimization. Testing in simulation has shown the IPPC to be capable of reducing PHEV energy consumption by 4%–10% across real-world and standard drive cycles. In addition, the presented IPPC was deployed onto a rapid prototyping embedded controller where on-road, real-time testing has shown the IPPC to be capable of providing an energy reduction of 5%, thus confirming the energy savings observed in simulation.

Application of Optimal Control Method to Path Tracking Problem of Vehicle

Application of Optimal Control Method to Path Tracking Problem of Vehicle

Constrained Buckling of Spatial Elastica: Application of Optimal Control Method

A post-buckling analysis of a constant or variable length spatial elastica constrained by a cylindrical wall is performed for a first time by adopting an optimal control methodology. Its application in a constrained buckling analysis is shown to be superior when compared to other numerical techniques, as the inclusion of the unilateral constraints is feasible without the need of any special treatment or approximation. Furthermore, the formulation is simple and the optimal configurations of the spatial elastica can be also obtained by considering the minimization condition of the Hamiltonian. We first present the optimal control formulation for the constrained buckling problem of a constant length spatial elastica, including its associated necessary optimality conditions that constitute the Pontryagin's minimum principle. This fundamental constrained buckling problem is used to validate the proposed methodology. The general buckling problem of a variable length spatial elastica is then analyzed that consists of two parts; (1) the solution of the optimal control problem that involves the inserted elastica inside the conduit and (2) the derivation of the buckling load by taking into account the generation of the configurational or Eshelby-like force at the insertion point of the sliding sleeve. A variety of examples are accordingly presented, where the effects of factors, such as the presence of uniform pressure, the clearance of the wall, and the torsional rigidity, on the buckling response of the spatial elastica, are investigated.

Application of optimal control method in buckling analysis of constrained elastica problems

Main concern of the paper is to illustrate the generic application of the optimal control method in the analysis of a constrained buckling problem and its simple and elegant formulation when compared to other techniques. Thus, an optimal control methodology is adopted to investigate the buckling behavior of thin elastic structures under the presence of unilateral constraints. We particularly explore the post-buckling response of a constant or variable length elastica constrained by rigid walls.The equivalence between the calculus of variations and the optimal control is first demonstrated, with a main focus on the post-buckling behavior of a constant length elastica which is constrained by horizontal walls. The gradual construction of the constrained buckling problem as an optimal control problem from its equivalent Lagrangian form clearly shows its advantage when compared to the calculus of variations. The necessary optimality conditions, which constitute the Pontryagin’s minimum or maximum principle, are also derived by considering a direct adjoining approach. The solution of the optimal control problem is then performed by applying a direct method.Validation of the methodology is first achieved by reproducing examples available in the literature. Then the effects of factors, such as the geometry of the walls and the variability in the bending stiffness of the elastica, on its buckling response are analyzed. These constrained buckling problems are investigated for the first time, while through them it is also readily shown that the presence of geometric or material nonlinearity does not introduce any essential complexity in the buckling analysis when the optimal control methodology is adopted.

Computing interval-valued reliability measures: application of optimal control methods

The paper describes an approach to deriving interval-valued reliability measures given partial statistical information on the occurrence of failures. We apply methods of optimal control theory, in particular, Pontryagin’s principle of maximum to solve the non-linear optimisation problem and derive the probabilistic interval-valued quantities of interest. It is proven that the optimisation problem can be translated into another problem statement that can be solved on the class of piecewise continuous probability density functions (pdfs). This class often consists of piecewise exponential pdfs which appear as soon as among the constraints there are bounds on a failure rate of a component under consideration. Finding the number of switching points of the piecewise continuous pdfs and their values becomes the focus of the approach described in the paper. Examples are provided.

Optimal control on decoherence for Markovian quantum system

The optimal control function for an open system is designed by taking the expectation value of the state under control reaching the target state as the performance indicator. Utilizing Runge Kutta method, the open quantum system with a superconducting qubit is numerically investigated. The influences of parameters on the population transfer probability and control time are compared. The simulation experiments indicate that the state transfer and state maintenance is realized with a success rate of more than 98 percent, which validating the application of optimal control method in the state transfer and maintenance for open quantum system containing superconducting qubits. Moreover, proper increase of the proportional coefficients can accelerate the qubits flip and reduce the vibration frequency of control function.

Optimal Control of Vitrifying Polymers Cooling Process to Reduce Residual Stresses

This paper’s purpose is analyzing a problem of optimal control of vitrifying polymers cooling process. A solution methodology is suggested targeting at decreasing technological residual stresses, which predetermine major operating characteristics of polymer structures. The criteria function to be minimized is maximum intensity of technological residual stresses under minimum cooling process time. To determine technological residual stresses, a problem of thermoviscoelasticity is solved in finite-elemental formulation. The chosen mathematical model of mechanical behavior of viscoelastic bodies under thermorelaxation transition allows to formulate and to solve the problem of cooling process optimal control. Obtained results show that application of optimal control methods to vitrifying polymers cooling process lead to tenfold decrease of technological residual stresses when compared to an uncontrolled process.

Adaptive management of environmental acidity

This paper proposes a new approach to the determination of doses of some meliorant for stabilizing the environmental acidity in an agricultural field through the application of optimal control methods in the presence of random disturbances. The acidity dynamics process is considered as a partially observable Markov process, and the Bayesian approach is applied.

Optimal control methods in inverse problems and computational processes

Abstract - The paper presents results related to application of optimal control methods to investigation and solution of a class of inverse problems. Applications of these methods to particular inverse problems are considered.

Computationally efficient optimal control methods applied to power systems

Attention is focused on the efficient application of optimal control methods with structural constraints to large power systems. A novel framework for linear-quadratic regulator problems is proposed which allows the use of matrix and vector sparsity techniques in the solution process. Results concerning the proposed method's computational performance as applied to a 750-bus, 40-machine power networks are provided.

Application of optimal control methods for constructing finite difference schemes in hydrodynamics

Article Application of optimal control methods for constructing finite difference schemes in hydrodynamics was published on January 1, 1994 in the journal Russian Journal of Numerical Analysis and Mathematical Modelling (volume 9, issue 4).

Optimal stochastic control in natural resource management: framework and examples

A framework is presented for the application of optimal control methods to natural resource problems. An expression of the optimal control problem appropriate for renewable natural resources is given and its application to Markovian systems is presented in some detail. Three general approaches are outlined for determining optimal control of infinite time horizon systems and three examples from the natural resource literature are used for illustration.

Approaches to modelling and optimal control of a micro-alternator system

The paper describes a practical study of the application of optimal control methods to provide integrated control of the exciter and turbine of a laboratory model turbo-alternator system. Recursive least-squares identification techniques are employed to generate linear multivariate state and input/output models of the non-linear system dynamics. Comparative experimental system performance with both state and output feedback controllers, synthesized from corresponding analytical and identified models, is evaluated over a wide range of operating conditions. The results emphasize the effectiveness of integrated optimal controllers in the power system environment, and, in particular, their reduced sensitivity to changes in operating conditions and transmission line impedance compared with conventional control configurations.