Abstract: A model predictive controller for a performing stateless prediction. Using dosed form algebraic expressions for the step test in a dynamic matrix eliminates the requirement for individual calculation on each element. With both the dynamic matrix and the vector of predicted errors written in terms of discrete algebraic equations, the control law is written as a function of the current state of the system. The control law is then be reduced to its minimal form, which leaves the next control action to be a function of the system parameters, the past errors, and the past control actions. Since the system parameters are constant, this controller is then be reduced into a single discrete equation. This greatly reduces the computations required in each control loop iteration.

Abstract: MPC controller systems and methods are disclosed that may be applied to various different types of MPC controllers and methods for use in any desired or appropriate controller settings, such as physical systems with non-linearities that require careful modelling tactics, which are considered complex systems. Complex systems include fast systems which are those where solving the descriptive model of the system has the potential to surpass the closed loop sampling time required to control the system. Examples of complex fast systems are robot manipulators, quadracopters and injection speed of an injection molding process.

Abstract: A model predictive controller for a performing stateless prediction. Using dosed form algebraic expressions for the step test in a dynamic matrix eliminates the requirement for individual calculation on each element. With both the dynamic matrix and the vector of predicted errors written in terms of discrete algebraic equations, the control law is written as a function of the current state of the system. The control law is then be reduced to its minimal form, which leaves the next control action to be a function of the system parameters, the past errors, and the past control actions. Since the system parameters are constant, this controller is then be reduced into a single discrete equation. This greatly reduces the computations required in each control loop iteration.

Abstract: A method or system for industrial process optimization, including sensing data about an industrial process cycle that operates according to a set of process setpoints; determining a target performance for the process cycle based on the sensed data; selecting process set-points from a hypothesis space for a future industrial process cycle based on the target performance, and updating the hypothesis space.

Abstract: A new MPC controller design and method that may be applied to various different types of model predictive control (MPC) controllers and methods for use in any desired or appropriate controller setting, such as for plants, and can be imposed on conventional predictive control schemes to provide tighter control when tracking complex setpoint trajectories. The method introduces a correction parameter ?, independent of plant gain, evaluated online at each control timestep to drive the plant output to its reference more accurately than the original schemes. In a linear system, the correction parameter converges to a constant when optimized (evaluated on-line). The new MPC controller and method can also be applied on a nonlinear system. In a non-linear system, where the time constant is non-linear, the correction parameter will be variable.

Abstract: A new MPC controller design and method that may be applied to various different types of model predictive control (MPC) controllers and methods for use in any desired or appropriate controller setting, such as for plants, and can be imposed on conventional predictive control schemes to provide tighter control when tracking complex setpoint trajectories. The method introduces a correction parameter ?, independent of plant gain, evaluated online at each control timestep to drive the plant output to its reference more accurately than the original schemes. In a linear system, the correction parameter converges to a constant when optimized (evaluated on-line). The new MPC controller and method can also be applied on a nonlinear system. In a non-linear system, where the time constant is non-linear, the correction parameter will be variable.

Abstract: A computer implemented method of conducting closed-loop control of a physical system comprising the steps of carrying out an initialization of the physical system to commencing closed-loop control, evaluating the optimal constrained control move using the system error and the initial normalized matrix using a control move solver; calculating a first control action by the sum of delta u(0) and the initial control action; and implementing the result to the physical system by converting the control action to an output control signal to effect a change in at least one operating variable.

Abstract: A computer implemented method of conducting closed-loop control of a physical system comprising the steps of carrying out an initialization of the physical system to commencing closed-loop control, evaluating the optimal constrained control move using the system error and the initial normalized matrix using a control move solver; calculating a first control action by the sum of delta u(0) and the initial control action; and implementing the result to the physical system by converting the control action to an output control signal to effect a change in at least one operating variable.