A system for controlling a plurality of hydraulic effectors operably connected to an engine to control engine parameters. The system also includes a plurality of sensors operably connected to measure a state or parameter of each effector, a pump configured to supply fluid to the plurality of effectors, and a controller operably connected to the plurality of sensors, the plurality of effectors, and the pump. The controller executes a method for an adaptive model-based control for controlling each effector, The method includes receiving a request indicative of a desired state for each effector, receiving a weighting associated each request, obtaining information about a current state of each effector, and updating an adaptive model based control (MBC) based upon the information. The method also includes generating a control command for an effector based upon the adaptive MBC and commanding the effector based upon the control command.

A system for controlling a plurality of electromechanical effectors operably connected to an engine to control engine parameters. The system also includes a plurality of sensors operably connected to measure a state or parameter of each effector, a power supply configured to supply power to the plurality of effectors, and a controller operably connected to the plurality of sensors, the plurality of effectors, and the power supply. The controller executes a method for an adaptive model-based control for controlling each effector, The method includes receiving a request indicative of a desired state for each effector, receiving a weighting associated each request, obtaining information about a current state of each effector, and updating an adaptive model based control (MBC) based upon the information. The method also includes generating a control command for an effector based upon the adaptive MBC and commanding the effector based upon the control command.

A method of partial-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, includes establishing a dynamic data model of a controlled plant subject to measurable disturbances, wherein the dynamic data model is described by a pseudo Jacobian input matrix and a pseudo Jacobian disturbance matrix; constructing cost functions and solving their optimization problems to find optimal values of the pseudo Jacobian input matrix and the pseudo Jacobian disturbance matrix; designing a partial-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances; constructing an energy function and solving it by using a momentum gradient descent method to find optimal values of the partial-form adaptive input matrix and the partial-form adaptive disturbance matrix; controlling the controlled plant by using the control law. The control method of the present invention provides significant improvements in disturbance compensation control performance and achieves effective tracking of desired system outputs.

The invention provides a multi-input multi-output (MIMO) control system comprising a controller adapted for receiving an input set of at least two control input parameters and a set of at least two control output parameters, said control system arranged for effecting a modified deadbeat control, in which said modified deadbeat control comprises a robust deadbeat control for an n-th order, linear time invariant (LTI) system based upon a series of cascade proportional integrating-differentiating (PID) controls, each PID control comprising a system transfer function having a nominator and a denominator, wherein for the nominator a constant gain (K) is selected for each PID control. The invention further provides a method for controlling a continuous process using this control system.