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Advanced process control assignment help from experienced tutors

Advanced process control can be challenging. However, with our experts' help, we are here to ensure that you have the help you need. Therefore, if you are looking for advanced process control assignment help, we can offer it. Having been in this field for more than a decade, we understand what would make the best grades. Note that whether your assignment is urgent or long-term, we are here to ensure that it is completed on time. Therefore, by hiring an expert from us, you can be sure that you will get a timely, affordable, and high-quality solution.

The exhaustive list of topics in Advanced Process Control in which we provide Help with Homework Assignment and Help with Project is as follows:

  • Linear Dynamic Models for Advanced Control
  • Dynamic models in chemical engineering and linearization.
  • Linear continuous time state space models and Laplace.
  • Transfer function matrix representation.
  • Computer oriented (or discrete time) state space models and z-transfer function matrix representation.
  • Development of discrete time state space models from input-output data (development of OE and ARMAX models, state realizations).
  • Analysis of State Space Models
  • State transformations , poles and zeros, characteristic equation.
  • Solution of unforced and forced linear differential and difference equations and asymptotic behavior of solutions.
  • Lyapunov stability analysis.
  • Observer design
  • Observability and observervable canonical form, Luenberger (SISO) observer and pole placement design, Prediction and current state observer, reduced order observer.
  • Observer design in presence of state and measurement noise, Kalman filtering and optimal state estimation, convergence of observer error, connection between Kalman filter and linear time series models.
  • State feedback controller design
  • Controllability, reachability and controllable canonical form.
  • State feedback controller for SISO systems design by pole placement, difficulties in extending to multivariable systems.
  • Linear quadratic optimal control (Derivation of Riccati equations, set point tracking and disturbance rejection, stability analysis).
  • Separation principle and state feedback control using state observers.
  • Examples of state LQ and LQG.
  • Model Predictive Control
  • Limitations of LQ control and operating constraints.
  • Dynamic matrix control (state space formulation, unconstrained solution, QP formulation), Internal Model Control.
  • Model predictive control (MPC) based on state estimation (Kalman filtering).
  • Nominal stability and robustness of MPC.
  • MPC case study.
  • Beyond linear multivariable control.