In this work, a predictive control framework is proposed for the constrained stabilization of switched nonlinear systems that transit between their constituent modes at prescribed switching times. The main idea to design Lyapunov-based controller each mode in which system operates and incorporate constraints upon satisfaction ensure transitions occur way guarantees stability closed-loop system. This achieved as follows: For mode, model (MPC) designed, an analytic bounded controller, using...

In this work, we present a novel, data‐driven, quality modeling, and control approach for batch processes. Specifically, adapt subspace identification methods use with data to identify state‐space model from available process measurements input moves. We demonstrate that the resulting linear time‐invariant (LTI), dynamic, is able describe transient behavior of finite duration Next, relate terminal value identified states. Finally, apply in shrinking‐horizon, predictive scheme directly...

Abstract The problem of implementing fault‐tolerant control to nonlinear processes with input constraints subject actuator failures is considered, and an approach predicated upon the idea integrating fault‐detection, feedback supervisory presented demonstrated. To illustrate main behind proposed approach, availability measurements all process state variables initially assumed. For under consideration, a family candidate configurations, characterized by different manipulated inputs, first...

This work considers the problem of stabilization nonlinear systems subject to constraints, uncertainty and faults in control actuator. We first design a robust model predictive controller that allows for an explicit characterization set initial conditions starting from where feasibility optimization closed-loop stability is guaranteed. The main idea designing employ Lyapunov-based techniques formulate constraints (a) explicitly account law, without making computationally intractable, (b)...

Abstract This work focuses on predictive control of linear parabolic partial differential equations (PDEs) with state and constraints. Initially, the PDE is written as an infinite‐dimensional system in appropriate Hilbert space. Next, modal decomposition techniques are used to derive a finite‐dimensional that captures dominant dynamics system, express constraints terms A number model (MPC) formulations, designed basis different approximations, then presented compared. The closed‐loop...

This paper addresses the problem of synergizing first-principles models with data-driven models. is achieved by building a hybrid model where subspace identification algorithm used to create for residuals (mismatch in outputs generated and plant output) rather than being dynamic process outputs. A continuous stirred tank reactor (CSTR) setup illustrate proposed approach on system. To further evaluate its efficacy, methodology applied batch poly(methyl methacrylate) (PMMA) polymerization...

This work focuses on the modelling, simulation and control of a batch protein crystallization process that is used to produce crystals tetragonal hen egg-white (HEW) lysozyme. First, model presented describes formation via nucleation growth. Existing experimental data are develop empirical models growth mechanisms HEW lysozyme crystal. The developed rate expressions within population balance simulate process. Then, reduction techniques derive reduced-order moments for purpose controller...

Abstract The problem of control nonlinear process systems subject to input constraints and sensor faults (complete failure or intermittent unavailability measurements) is considered. A fault‐tolerant controller designed that utilizes reconfiguration (switching an alternate configuration) in a way accounts for the nonlinearity, presence occurrence faults. To clearly illustrate importance accounting constraints, first necessitate recovery maintain closed‐loop stability We address determining,...

Abstract A data‐based multimodel approach is developed in this work for modeling batch systems which multiple local linear models are identified using latent variable regression and combined an appropriate weighting function that arises from fuzzy c ‐means clustering. The resulting model used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states where can be driven inside a desired end‐point neighborhood system), subsequently incorporated predictive...

Abstract This work considers the problem of control system/actuator failures in nonlinear processes subject to input constraints and presents two approaches for fault‐tolerant that focus on incorporating performance robustness considerations, respectively. In both approaches, first a family candidate configurations, characterized by different manipulated inputs, is identified process under consideration. Performance considerations are incorporated via design Lyapunov‐based predictive...