초록 ▼

Methods and systems for the design and implementation of optimal multivariable MPC controllers for fast-sampling constrained dynamic systems utilizing a primal-dual feasibility approach and/or a graph approach. The primal-dual feasibility approach can compute and store matrices defining constraints

Methods and systems for the design and implementation of optimal multivariable MPC controllers for fast-sampling constrained dynamic systems utilizing a primal-dual feasibility approach and/or a graph approach. The primal-dual feasibility approach can compute and store matrices defining constraints of quadratic programming problems in an off-line part in order to calculate vectors of Lagrange multipliers and an optimizer. Then primal-dual feasibility can be checked in an on-line part using the Lagrange multipliers and the optimizer can provide a unique optimal solution for the constrained dynamic system. The graph approach can compute and store the matrices and the vectors, and also prepare and store a structure of directed graph in off-line part. An optimizer for a given parameter vector can be determined in on-line part using the directed graph, the matrices and the vectors.

대표청구항 ▼

1. A Multi-Parameter Controller (MPC) for use in controlling one or more components of an automotive system, the automotive system having one or more sensors and one or more actuators, said Multi-Parameter Controller (MPC) comprising: one or more inputs for receiving one or more sensor signals from

1. A Multi-Parameter Controller (MPC) for use in controlling one or more components of an automotive system, the automotive system having one or more sensors and one or more actuators, said Multi-Parameter Controller (MPC) comprising: one or more inputs for receiving one or more sensor signals from one or more sensors of the automotive system;one or more outputs for providing one or more control outputs to one or more actuators of the automotive system;a controller coupled to the one or more inputs and the one or more outputs of the Multi-Parameter Controller (MPC), the controller implementing an explicit quadratic programming solver having an off-line part and an on-line part, said explicit quadratic programming solver configured to solve at least one explicit quadratic programming problem utilizing at least one of a primal-dual feasibility algorithm and a graph algorithm, wherein said explicit quadratic programming solver computes the one or more control outputs based, at least in part, on one or more of the sensor signals received via the one or more inputs of the Multi-Parameter Controller (MPC) and on a plurality of state variables and a plurality of matrices stored in said off-line part of said explicit quadratic programming solver; andthe controller provides the one or more control outputs on one or more of the outputs of the Multi-Parameter Controller (MPC) to control said one or more components of the automotive system. 2. The Multi-Parameter Controller (MPC) of claim 1, wherein said primal-dual feasibility algorithm comprises: computing and storing said plurality of matrices in said off-line part of said explicit quadratic programming solver in order to calculate a plurality of vectors of Lagrange multipliers and an optimizer in said on-line part of said explicit quadratic programming solver; andchecking a primal-dual feasibility of said plurality of vectors in said on-line part of said explicit quadratic programming solver utilizing said Lagrange multipliers and said optimizer to determine a desirable solution for said one or more components of the automotive system. 3. The Multi-Parameter Controller (MPC) of claim 1, wherein said graph algorithm comprises: computing and storing said plurality of matrices, and constructing and storing a directed graph in said off-line part of said explicit quadratic programming solver, wherein said directed graph is associated with a plurality of control laws and a plurality of constraints;determining a set of feasible control laws utilizing said directed graph; anddetermining an optimal control law within said set of feasible control laws in said on-line part of said explicit quadratic programming solver. 4. The computer-implemented method of claim 3 wherein said plurality of matrices are defined for one or more feasible combinations of said plurality of constraints. 5. The Multi-Parameter Controller (MPC) of claim 1, wherein the controller further implements a state observer for determining a plurality of values associated with the one or more components of the automotive system in order to generate the plurality of state variables of the one or more components of the automotive system. 6. The Multi-Parameter Controller (MPC) of claim 5, wherein the plurality of values associated with the one or more components of the automotive system comprise a plurality of present and/or past values associated with at least one of the actuators and at least one of the sensors of the automotive system. 7. The Multi-Parameter Controller (MPC) of claim 1, wherein the off-line part of the explicit quadratic programming solver is configured to solve a model predictive control problem and deliver with respect to said primal-dual feasibility algorithm, a data structure comprising vectors and matrices defining constraints related to the explicit quadratic programming problem. 8. The Multi-Parameter Controller (MPC) of claim 1, wherein the off-line part of the explicit quadratic programming solver is configured to solve a model predictive control problem and deliver with respect to said graph algorithm, a plurality of matrices, a plurality of vectors and a structure containing a directed graph. 9. The Multi-Parameter Controller (MPC) of claim 1, wherein the one or more components of the automotive system include at least part of a power train of an automobile. 10. The Multi-Parameter Controller (MPC) of claim 1, wherein the one or more components of the automotive system include an engine. 11. The Multi-Parameter Controller (MPC) of claim 1, wherein the one or more components of the automotive system include a turbocharger. 12. The Multi-Parameter Controller (MPC) of claim 1, wherein the Multi-Parameter Controller (MPC) is updated at each of a series of sequential sample times, and wherein the on-line part of the explicit quadratic programming solver updates the one or more control outputs at each of the sample times using said primal-dual feasibility algorithm and/or said graph algorithm. 13. The Multi-Parameter Controller (MPC) of claim 12, wherein the off-line part of the explicit quadratic programming solver is not updated at each of the sample times. 14. A Multi-Parameter Controller (MPC) for use in controlling one or more components of an engine, the engine having one or more sensors and one or more actuators, said Multi-Parameter Controller (MPC) comprising: one or more inputs for receiving one or more sensor signals from one or more sensors of the engine;one or more outputs for providing one or more control outputs to one or more actuators of the engine;a controller coupled to the one or more inputs and the one or more outputs of the Multi-Parameter Controller (MPC), the controller implementing an explicit quadratic programming solver having an off-line part and an on-line part, said explicit quadratic programming solver configured to solve at least one explicit quadratic programming problem utilizing a primal-dual feasibility algorithm, wherein said explicit quadratic programming solver computes the one or more control outputs based, at least in part, on one or more of the sensor signals received via the one or more inputs of the Multi-Parameter Controller (MPC) and on a plurality of state variables and a plurality of matrices stored in said off-line part of said explicit quadratic programming solver; andthe controller provides the one or more control outputs on one or more of the outputs of the Multi-Parameter Controller (MPC) to control said one or more components of the engine. 15. The Multi-Parameter Controller (MPC) of claim 14, wherein said primal-dual feasibility algorithm comprises: computing and storing said plurality of matrices in said off-line part of said explicit quadratic programming solver in order to calculate a plurality of vectors of Lagrange multipliers and an optimizer in said on-line part of said explicit quadratic programming solver; andchecking a primal-dual feasibility of said plurality of vectors in said on-line part of said explicit quadratic programming solver utilizing said Lagrange multipliers and said optimizer to determine a desirable solution for said one or more components of the engine. 16. A Multi-Parameter Controller (MPC) for use in controlling one or more components of an engine, the engine having one or more sensors and one or more actuators, said Multi-Parameter Controller (MPC) comprising: one or more inputs for receiving one or more sensor signals from one or more sensors of the engine;one or more outputs for providing one or more control outputs to one or more actuators of the engine;a controller coupled to the one or more inputs and the one or more outputs of the Multi-Parameter Controller (MPC), the controller implementing an explicit quadratic programming solver having an off-line part and an on-line part, said explicit quadratic programming solver configured to solve at least one explicit quadratic programming problem utilizing a graph algorithm, wherein said explicit quadratic programming solver computes the one or more control outputs based, at least in part, on one or more of the sensor signals received via the one or more inputs of the Multi-Parameter Controller (MPC) and on a plurality of state variables and a plurality of matrices stored in said off-line part of said explicit quadratic programming solver; andthe controller provides the one or more control outputs on one or more of the outputs of the Multi-Parameter Controller (MPC) to control said one or more components of the engine. 17. The Multi-Parameter Controller (MPC) of claim 16, wherein said graph algorithm comprises: computing and storing said plurality of matrices, and constructing and storing a directed graph in said off-line part of said explicit quadratic programming solver, wherein said directed graph is associated with a plurality of control laws and a plurality of constraints;determining a set of feasible control laws utilizing said directed graph; anddetermining an optimal control law within said set of feasible control laws in said on-line part of said explicit quadratic programming solver.