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This work addresses the problem of designing an offset-free implementable reinforcement learning (RL) controller for nonlinear processes. RL-based controllers can update the control policy using observed data obtained online based on the controller-process interactions. This allows to alleviate the regular model maintenance step that is essential in advanced control techniques such as model predictive control (MPC). However, random explorations are required for an RL agent to find optimal state–action regions so as to finally reach the optimal policy. This is not implementable in practical situations due to safety concerns and economic objectives. To address this issue, a pre-training strategy is proposed to provide a secure platform for online implementations of the RL controllers. To this end, an offset-free MPC (representative industrial MPC) optimization problem is leveraged to train the RL agent offline. Having obtained similar performance to the offset-free MPC, the RL agent is utilized for online control to interact with the actual process. The efficacy of the proposed approach to handle nonlinearity and changes in plant operating conditions (due to unmeasured disturbances) are demonstrated through simulations on a chemical reactor example for a pH neutralization process. The results show that the proposed RL controller can significantly improve the oscillatory closed-loop responses, obtained by running the offset-free MPC due to the plant-model mismatch and unmeasured disturbances. • This work proposes a practical reinforcement (RL) learning control strategy. • An RL agent is pre-trained using offset-free model predictive control (MPC) information. • The RL controller can initially replicate the offset-free MPC performance. • The pre-trained RL controller outperforms the offset-free MPC after online training. [ABSTRACT FROM AUTHOR]

This work introduces a methodology to develop linear data-driven steady-state models of bi-product distillation towers for use in operations monitoring and control. Linear monitoring models derived from measurements that are typically used for composition control (reboiler duty/bottoms flow, reflux flow/distillate flow, and one temperature measurement per tower section) have low accuracy due to the nonlinear behavior of distillation towers. We show that the addition of a judiciously selected second temperature measurement in each section leads to a highly accurate linear model. These temperatures are selected via an iterative procedure based on analyzing the residuals' coefficient plots of PLS models. Remarkably, the models accurately predict product compositions, even without knowing the feed composition. Prediction of product composition for the multicomponent mixture feeds requires that the product flows to be included in the model. Our results demonstrate that the nonlinearity of the data-driven models can be reduced or eliminated by the proper selection of the model features. [Display omitted] • Methodology for selection of temperature measurements for monitoring models. • Determination of the required number of temperature sensors based on cross-validation. • Residual coefficient plots used as selection criteria. • Addition of two temperature measurement in each section increases accuracy. • Additional flow measurements required for multicomponent systems. [ABSTRACT FROM AUTHOR]

In this work, we develop models and a fault detection and isolation (FDI) methodology for heating, ventilation and air conditioning (HVAC) systems that utilizes recurrent neural networks (RNN). The FDI design does not require the existence of plant fault history, mechanistic models or a set of expert rules to isolate faults. The key is to first use plant data to build predictive models and input/output estimators, and then embed them within FDI filters. A distributed FDI framework is designed consisting of local FDI (LFDI) schemes that communicate with each other for improved FDI. The distributed FDI framework enables diagnosis of multiple faults in different components of the HVAC system when a fault in one of the control components directly affects the other subsystems. The effectiveness of the proposed FDI scheme is shown via simulation examples on a simulation test bed, as well as using real data. The simulations revealed superior performance of the proposed FDI methodology over FDI approaches using subspace based models for both simulation and real data cases. [ABSTRACT FROM AUTHOR]

Highlights • Utilizes data variety in subspace identification and control of batch processes. • The modeling approach is able to use data variety for batches of unequal lengths. • A novel model predictive control (MPC) with explicit model validity constraints, and the ability to specify target PSD is proposed. • The approach is demonstrated on batch crystallization process. Abstract This manuscript illustrates the use of big data for modeling and control of batch processes. A modeling and control framework is presented that utilizes data variety (temperature or concentration measurements along with size distribution) to achieve newer control objectives. For an illustrative crystallization process, an approach is proposed consisting of a subspace state-space model augmented with a linear quality model, able to model and predict, and therefore control the particle size distribution (PSD). The identified model is deployed in a linear model predictive control (MPC) with explicit model validity constraints. The paper presents two formulations: a) one that minimizes the volume of fines in the product by leveraging the variety of measurements and b) the other that directly controls the shape of the particle size distribution in the product. The former case is compared to traditional control practice while the latter's superior ability to achieve desired PSD shape is demonstrated. [ABSTRACT FROM AUTHOR]

Highlights • Utilizes data variety in subspace identification and control of batch processes. • The modeling approach is able to use data variety for batches of unequal lengths. • A novel model predictive control (MPC) with explicit model validity constraints, and the ability to specify target PSD is proposed. • The approach is demonstrated on batch crystallization process. Abstract This manuscript illustrates the use of big data for modeling and control of batch processes. A modeling and control framework is presented that utilizes data variety (temperature or concentration measurements along with size distribution) to achieve newer control objectives. For an illustrative crystallization process, an approach is proposed consisting of a subspace state-space model augmented with a linear quality model, able to model and predict, and therefore control the particle size distribution (PSD). The identified model is deployed in a linear model predictive control (MPC) with explicit model validity constraints. The paper presents two formulations: (a) one that minimizes the volume of fines in the product by leveraging the variety of measurements and (b) the other that directly controls the shape of the particle size distribution in the product. The former case is compared to traditional control practice while the latter's superior ability to achieve desired PSD shape is demonstrated. [ABSTRACT FROM AUTHOR]

In this work, we address the problem of simultaneous fault diagnosis in nonlinear uncertain networked systems utilizing a distributed fault detection and isolation (FDI) strategy. The key idea is to design a bank of local FDI (LFDI) schemes that communicate with each other for improved FDI. The proposed distributed FDI scheme is shown to be able to handle local faults as well as those that affect more than one subsystem. This is achieved via appropriate adaptation of the LFDI filters based on information exchange with other subsystems and using the proposed notion of detectability index. The detectability index and isolability conditions are rigorously derived for the distributed FDI scheme. Effectiveness of the proposed methodology is shown via application to a reactor-separator process subject to uncertainty and measurement noise. [ABSTRACT FROM AUTHOR]

This work addresses the problem of simultaneous actuator and sensor fault detection and isolation (FDI) for control affine nonlinear uncertain systems in the absence of measurement noise. The FDI is achieved by using a bank of filters, which utilize a subset of the measurements along with prescribed values of the control actuators to estimate states and compute expected process behavior. Residuals are next defined as the difference between the observed and expected behavior. Detectability conditions are developed, which, upon satisfaction, ensure that each residual remains sensitive to a subset of fault scenarios in the presence of uncertainty. To this end, first the ability of observers in providing bounded estimation error for a generalized class of nonlinear uncertain systems is rigorously established. These bounds allow determining thresholds that account for the impact of uncertainty on each residual. Finally, the ability of the proposed framework to achieve FDI by ensuring a unique residual breaching pattern for each fault scenario is established. The efficacy of the FDI framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example. [ABSTRACT FROM AUTHOR]

Summary: This work addresses the problem of output feedack control of nonlinear uncertain systems via adaptive Lyapunov‐based model predictive control design. To this end, at every control implementation, a moving horizon mechanism is first utilized to generate current estimates of the uncertainty and states. The model with the current estimated uncertainty is then used in a Lyapunov‐based model predictive controller to achieve uncertainty rejection. The key ideas are explained through an illustrative example and the application demonstrated on a networked reactor‐separator process subject to measurement noise and uncertainty. [ABSTRACT FROM AUTHOR]

In this work, we address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are then the new model is utilized within the MPC. The effectiveness of the proposed method is illustrated through simulations on a nonlinear polymerization reactor. [ABSTRACT FROM AUTHOR]

This work presents an integrated framework for fault detection and isolation (FDI) and fault tolerant control (FTC) of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems. To this end, first a statistical model based FDI framework is designed using existing techniques such as principal component analysis (PCA) and joint angle analysis as a benchmark for comparison. Then a novel linear causal model based framework for FDI of multiple actuator and multiple sensor faults is designed and implemented and shown to possess superior FDI capabilities compared to the statistical model based framework. Finally, a safe parking strategy is designed and the ensuing energy savings for the case of stuck dampers demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for control of input-constrained nonlinear systems that offers guaranteed stabilization from the entire null controllable region (NCR). The controller achieves stabilization by using a constrained control Lyapunov function (CCLF) based on this NCR. Prior to online implementation, the level sets the CCLF are constructed using an iterative algorithm. The algorithm works by using an invariance principle to expand an initial quadratic Lyapunov function-based region of attraction. The level sets of this CCLF are then utilized in the control calculations, and in particular, an MPC is formulated that requires the system to go to lower level sets of the CCLF. The proposed MPC thus achieves stabilization from the entire NCR. The proposed approach is first corroborated against existing results for linear systems using two- and three-dimensional linear systems examples. Subsequently, the implementation is shown for two and three dimensional nonlinear systems. [ABSTRACT FROM AUTHOR]

In this work, we address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The efficacy of the proposed method and the various specific formulations needed are illustrated through implementation on a linear system subject to output feedback, a nonlinear CSTR subject to uncertainty and rate of change of input constraints, and a reactor separator system. The simulation results demonstrate significantly improved adherence to the prescribed performance criteria over a predictive controller representative of existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm to address the problem of guaranteed stabilization of nonlinear systems from the largest set of initial conditions from where stabilization is possible, the so-called null controllable region (NCR), while also handling state constraints and partial unboundedness of the NCR. The controller uses the geometry of the NCR to inform a constrained control Lyapunov function which in turn provides feasibility and stability guarantees. The NCR is produced from a novel iterative growth algorithm that exploits a certain invariance property. The new controller implementation is illustrated using a chemical reactor system and compared to a conventional nonlinear model predictive control (MPC) implementation. [ABSTRACT FROM AUTHOR]

• Distillation tower hybrid model. • Hybrid model with a multiplicative correction term. • Simplified first principles model reflects equipment structure. • Linear correction of model predictions for changes in equipment efficiency. • Hybrid model predicts well in extrapolation region and at varied efficiencies. In this work, we present a steady-state hybrid model composed of a simplified first principles model (SFPM) and an error correction model. The SFPM represents the internal structure of equipment and linear correction model, which is different from recent research using simpler forms of SFPM and nonlinear data-driven models. Retaining distillation tower structure in SFPM enables accurate calculation of sensitivities, composition correction model to be linear, and errors due to changes in tower efficiency can be corrected by an additional linear correction. This work uses multiplicative correction terms and shows that additive corrections often cause hybrid models to calculate negative concentrations. Due to the structure of the model, the parameters are estimated using PLS since PLS predictions were more accurate than neural networks. The correction models are linear, simple to update iteratively, and ensure accurate predictions for up to 10% changes in tower efficiency without updating the SFPM parameters. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of subspace-based model identification and predictive control of particulate process subject to uncertainty and time-varying parameters. To this end, subspace identification techniques are first adapted to handle the batch nature of the data. A linear model predictive controller (MPC) is next formulated to enable achieving a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open-loop policy, as well as a traditional trajectory tracking policy, using classical control. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints, as well as robustness to uncertainty. [ABSTRACT FROM AUTHOR]

Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for midbatch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number-average molecular weight, weight-average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentage errors (MAPE) in these variables are reduced from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%, 1.90%, and 1.67%, respectively. [ABSTRACT FROM AUTHOR]

In this manuscript, we consider the problem of multi-rate modeling and economic model predictive control (EMPC) of electric arc furnaces (EAF), which are widely used in the steel industry to produce molten steel from scrap metal. The two main challenges that we address are the multi-rate nature of the measurement availability, and the requirement to achieve final product of a desired characteristic, while minimizing the operation cost. To this end, multi-rate models are identified that include predictions for both the infrequently and frequently measured process variables. The models comprise local linear models and an appropriate weighting scheme to capture the nonlinear nature of the EAF. The resulting model is integrated into a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated cost. The EMPC is implemented on the EAF process and the closed-loop simulation results subject to the limited availability of process measurements and noise illustrate the improvement in economic performance over existing trajectory-tracking approaches. [ABSTRACT FROM AUTHOR]

Thiswork presents an offset-free model predictive control (OF-MPC)design for energy-efficient control of a heat pump. A model developedfrom a combination of first-principles and empirical components withparameters estimated using real heat pump data is used as a test bedfor the implementation of the MPC design. Open-loop system dynamicsare examined first to design and implement the appropriate controlstructure. Next, a linear model is identified using appropriate steptest simulations on the detailed heat pump model. Subsequently, amodel predictive controller formulation, which eliminates the tradeoffbetween tracking and energy objectives, is designed. The MPC includesan augmented model (including disturbance states) and an associatedLuenberger observer to estimate the disturbance (plant-model mismatchat steady state). Simulation results subject to realistic disturbancesand measurement noise demonstrate that energy savings can be achieved(anywhere between 1.7% and 1.9%), while preserving a safer operatingwindow (a 43% improvement, with respect to a safe operation measure)with the proposed OF-MPC design that achieves tighter tracking regulation(improvement of 40% and above), compared to a traditional controlapproach. [ABSTRACT FROM AUTHOR]

The present paper addresses the problem of designing a controller to enable stabilisation from the entire null controllable region (NCR) – the set of states from where stabilisation is possible). A recently developed approach for the numerical construction of the NCR is utilised, and first, the validity of the use of the NCR as a constrained control Lyapunov function (CCLF) is established. The analysis reveals the ability of a CCLF-based control design to drive the system to an equilibrium manifold. The CCLF-based control design is then utilised within a hybrid control framework that guarantees the ability to stabilise to the origin. Simulation results are used to illustrate the implementation of the control design. [ABSTRACT FROM AUTHOR]

Thiswork considers the problem of handling the failure of purificationequipment in a hydrogen production process. In this process, naturalgas and superheated steam are fed to a heated chemical reactor termeda reformer to produce hydrogen. The effluent gas is further processedand finally purified in a pressure swing adsorber (PSA). The off-gasout of the PSA is used to provide heat to the reformer. The failureof the PSA results in the loss of the off-gas, precluding the possibilityof the continuation of nominal operation. If not properly handled,this fault can lead to a shutdown of the entire plant. To achievestable operation while meeting operating requirements, a model predictivecontrol (MPC) based safe-parking framework is designed for the handlingof the fault. The key idea is to drive the process to a feasible operatingpoint that enables stable operation in the faulty mode. MPC is usedto handle the multivariable nature of the process and operating constraints.It also guides the process to a different operating region while meetingoperating requirements. The effectiveness of the safe-parking designis demonstrated through simulations using a first-principles modelof the hydrogen production unit. [ABSTRACT FROM AUTHOR]

Abstract: This work considers the problem of sensor fault isolation and fault-tolerant control for nonlinear systems subject to input constraints. The key idea is to design fault detection residuals and fault isolation logic by exploiting model-based sensor redundancy through a state observer. To this end, a high-gain observer is first presented, for which the convergence property is rigorously established, forming the basis of the residual design. A bank of residuals are then designed using a bank of observers, with each driven by a subset of measured outputs. A fault is isolated by checking which residuals breach their thresholds according to a logic rule. After the fault is isolated, the state estimate generated using measurements from the healthy sensors is used in closed-loop to maintain nominal operation. The implementation of the fault isolation and handling framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example. [Copyright &y& Elsevier]

SUMMARY This work considers linear systems with input constraints with the objective of designing a controller that guarantees stability from all initial conditions in the null-controllable region (the set of initial conditions from where the system can be stabilized). To this end, a recently developed procedure for construction of constrained control Lyapunov functions is utilized within a Lyapunov-based model predictive controller coupled with an auxiliary control design to achieve stabilization from all initial conditions in the null-controllable region. Illustrative simulation results as well as an application to a nonlinear chemical process example is presented to demonstrate the efficacy of the results.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an overview of results and future challenges related to temperature control and cost optimization in building energy systems. Control and economic optimization issues are discussed and illustrated through simulation examples. The paper concludes with results from model predictive control solutions and identification of important directions for future work. [Copyright &y& Elsevier]

Abstract: In this work, we generalize a previously developed data-based modeling methodology for batch processes to account for time-varying dynamics by incorporating online learning ability into the model, making it adaptive. First, the standard recursive least squares (RLS) algorithm with a forgetting factor is applied to update the model parameters. However, applying the standard RLS algorithm leads to a global update of all the models, which may be unnecessary depending on the operating conditions of the process. We address this issue by developing a probabilistic RLS (PRLS) estimator (also with a forgetting factor) for each model that takes the probability of the model being representative of the current plant dynamics into account in the update. The main advantage of adopting this localized update approach is adaptation tuning flexibility. Specifically, the model adaptations can be made more aggressive while maintaining better parameter precision compared to the standard RLS algorithm. The benefits from incorporating both RLS algorithms are demonstrated via simulations of a nylon-6,6 batch polymerization reactor. Closed-loop simulation results illustrate the improvement in tracking performance (over the non-adaptive model based design). The model adaptation is also shown to be crucial for achieving acceptable control performance when encountering large disturbances in the initial conditions. [Copyright &y& Elsevier]

Abstract: In this work, we design a Lyapunov-based model predictive controller (LMPC) for nonlinear systems subject to stochastic uncertainty. The LMPC design provides an explicitly characterized region from where stability can be probabilistically obtained. The key idea is to use stochastic Lyapunov-based feedback controllers, with well characterized stabilization in probability, to design constraints in the LMPC that allow the inheritance of the stability properties by the LMPC. The application of the proposed LMPC method is illustrated using a nonlinear chemical process system example. [Copyright &y& Elsevier]

This work considers the problem of handling actuator faults in switched nonlinear systems that transit between multiple modes subject to input constraints. The faults considered preclude the possibility of operation at the nominal equilibrium point in the active mode. For the case where the switching schedule is fixed, a safe-parking scheme is designed, which accounts for the switched nature, to operate the system at temporary equilibrium points during fault-repair time and allow resumption of nominal operation after fault repair. For the case where the switching schedule is adjustable, a safe-switching scheme is designed, which exploits the switched nature, to switch the system to a mode (if exists and available) where nominal operation can be preserved (through control structure reconfiguration when necessary) to continue nominal operation. The proposed framework is demonstrated via a switched process example, as well as application to a polymerisation process of methyl methacrylate. [ABSTRACT FROM AUTHOR]

Abstract: This work addresses the problem of handling actuator faults in a chemical plant. We consider a multi-unit nonlinear process system subject to input constraints and actuator faults in one unit that preclude the possibility of operating the unit at its nominal equilibrium point. The interconnected nature of the units in a plant brings forth unique opportunities as well as challenges that simply do not exist when handling faults in isolated units. In particular, the fact that the outlet streams from a faulty unit go through subsequent (well functioning) units raises the possibility of better restricting the effects of the fault to the faulty unit. At the same time, handling a fault in a unit may necessitate appropriate action in the downstream unit, which is not a result of a fault in the downstream unit. To address such issues that arise when handling faults in chemical plants, in this work we present a safe-parking framework (we define, in a previous work on handling faults in isolated units, a safe-park point as an operating point where in the event of a fault, a unit can be operated in a way that prevents onset of hazardous situation and allows smooth resumption of nominal operation) for plant-wide fault-tolerant control. We first consider the case where there exists a safe-park point for the faulty unit such that the effect of safe-parking can be completely rejected (via changing the nominal values of the manipulated variables) in the downstream unit. Steady-state as well as dynamic considerations (including the presence of input constraints) is used in determining the necessary conditions for safe-parking the multi-unit system. We next consider the problem where no viable safe-park point for the faulty unit exists such that its effect can be completely rejected in the subsequent unit. A methodology is presented to simultaneously safe-park the consecutive units. Finally, we incorporate performance considerations in the safe-parking framework and illustrate the implementation of the safe-parking framework using a multi-unit chemical reactor system. [Copyright &y& Elsevier]

This paper optimizes the design and operation of integrated distributed energy systems of large buildings and light industrial plants. The integration reduces greenhouse gas (GHG) emissions and the annual total cost (ATC) beyond the best possible from individual energy systems and there is a sharp optimum with respect to the size of the confectionary plant, which maximizes the reduction of GHG emissions and minimizes ATC. In contrast to previous studies, the design accounts for plant heating demands at different temperature levels and sets plant production volumes as decision variables. Optimal design, operation, and production schedule have been determined via a mixed-integer nonlinear programming model. Integrated energy systems of two entities (confectionary plant and residential building) have been compared to the non-integrated entities equipped with combined cooling, heating, and power systems. The lowest ATC (−8%) of the integrated system and the maximum GHG reductions (−8.3%) occur at slightly different sizes of the plant. Such reductions require simultaneous optimization of integrated design and operation of energy systems and relative sizes of the confectionary plant and the residential building. • Optimization approach for integrating energy systems in industries and buildings. • Connect each entity by heat and electricity exchange. • Heating demands at different temperatures are considered during the heat transfer. • Environmental and economic assessment of the integrated operation. • Integration of distributed energy systems reduces GHG emissions and costs. [ABSTRACT FROM AUTHOR]

Abstract: This work considers the problem of control of nonlinear process systems subject to input constraints and faults in the control actuators. Faults are considered that preclude the possibility of continued operating at the nominal equilibrium point and a framework (which we call the safe-parking framework) is developed to enable efficient resumption of nominal operation upon fault-recovery. To this end, first Lyapunov-based model predictive controllers, that allow for an explicit characterization of the stability region subject to constraints on the manipulated input, are designed. The stability region characterization is utilized in selecting ‘safe-park’ points from the safe-park candidates (equilibrium points subject to failed actuators). Specifically, a candidate parking point is termed a safe-park point if (1) the process state at the time of failure resides in the stability region of the safe-park candidate (subject to depleted control action), and (2) the safe-park candidate resides within the stability region of the nominal control configuration. Performance considerations, such as ease of transition from and to the safe-park point and cost of running the process at the safe-park point, are then quantified and utilized in choosing the optimal safe-park point. The proposed framework is illustrated using a chemical reactor example and robustness with respect to parametric uncertainty and disturbances is demonstrated on a styrene polymerization process. [Copyright &y& Elsevier]

Abstract: This work focuses on feedback control of particulate processes in the presence of sensor data losses. Two typical particulate process examples, a continuous crystallizer and a batch protein crystallizer, modeled by population balance models (PBMs), are considered. In the case of the continuous crystallizer, a Lyapunov-based nonlinear output feedback controller is first designed on the basis of an approximate moment model and is shown to stabilize an open-loop unstable steady-state of the PBM in the presence of input constraints. Then, the problem of modeling sensor data losses is investigated and the robustness of the nonlinear controller with respect to data losses is extensively investigated through simulations. In the case of the batch crystallizer, a predictive controller is first designed to obtain a desired crystal size distribution at the end of the batch while satisfying state and input constraints. Subsequently, we point out how the constraints in the predictive controller can be modified as a means of achieving constraint satisfaction in the closed-loop system in the presence of sensor data losses. [Copyright &y& Elsevier]

This work considers the problem of stabilization of control affine nonlinear process systems subject to constraints on the rate of change and magnitude of control inputs in the presence of uncertainty. We first handle rate constraints within a soft constraints framework. A new robust predictive controller formulation that minimizes rate constraint violation while guaranteeing stabilization and input constraint satisfaction from an explicitly characterized stability region is designed. We then derive conditions that allow for guaranteed satisfaction of hard rate constraints. Subsequently, a predictive controller is designed that ensures rate constraints satisfaction when the required conditions are satisfied, relaxing them otherwise to preserve feasibility and robust stability. The implementation of the proposed predictive controllers is illustrated via a chemical reactor example. [Copyright &y& Elsevier]

Abstract: This work considers the problem of control actuator fault detection and isolation and fault-tolerant control for a multi-input multi-output nonlinear system subject to constraints on the manipulated inputs and proposes a fault detection and isolation filter and controller reconfiguration design. The implementation of the fault detection and isolation filters and reconfiguration strategy are demonstrated via a chemical process example. [Copyright &y& Elsevier]

Abstract: This work focuses on fault-tolerant control of a gas phase polyethylene reactor. Initially, a family of candidate control configurations, characterized by different manipulated inputs, is identified. For each control configuration, a bounded nonlinear feedback controller, that enforces asymptotic closed-loop stability in the presence of constraints, is designed, and the constrained stability region associated with it is explicitly characterized using Lyapunov-based tools. Next, a fault-detection filter is designed to detect the occurrence of a fault in the control actuator by observing the deviation of the process states from the expected closed-loop behavior. A switching policy is then derived, on the basis of the stability regions, to orchestrate the activation/deactivation of the constituent control configurations in a way that guarantees closed-loop stability in the event of control system faults. Closed-loop system simulations demonstrate the effectiveness of the fault-tolerant control strategy. [Copyright &y& Elsevier]

Abstract: This work considers the problem of stabilization of nonlinear systems subject to state and control constraints, for cases where the state constraints need to be enforced at all times (hard constraints) and where they can be relaxed for some time (soft constraints). We propose a Lyapunov-based predictive control design that guarantees stabilization and state and input constraint satisfaction for all times from an explicitly characterized set of initial conditions. An auxiliary Lyapunov-based analytical bounded control design is used to characterize the stability region of the predictive controller and also provide a feasible initial guess to the optimization problem in the predictive controller formulation. For the case when the state constraints are soft, we propose a switched predictive control strategy that reduces the time during which state constraints are violated, driving the states into the state and input constraints feasibility region of the Lyapunov-based predictive controller. We demonstrate the application of the Lyapunov-based predictive controller designs through a chemical process example. [Copyright &y& Elsevier]

Abstract: This work presents a hybrid nonlinear control methodology for a broad class of switched nonlinear systems with input constraints. The key feature of the proposed methodology is the integrated synthesis, via multiple Lyapunov functions, of “lower-level” bounded nonlinear feedback controllers together with “upper-level” switching laws that orchestrate the transitions between the constituent modes and their respective controllers. Both the state and output feedback control problems are addressed. Under the assumption of availability of full state measurements, a family of bounded nonlinear state feedback controllers are initially designed to enforce asymptotic stability for the individual closed-loop modes and provide an explicit characterization of the corresponding stability region for each mode. A set of switching laws are then designed to track the evolution of the state and orchestrate switching between the stability regions of the constituent modes in a way that guarantees asymptotic stability of the overall switched closed-loop system. When complete state measurements are unavailable, a family of output feedback controllers are synthesized, using a combination of bounded state feedback controllers, high-gain observers and appropriate saturation filters to enforce asymptotic stability for the individual closed-loop modes and provide an explicit characterization of the corresponding output feedback stability regions in terms of the input constraints and the observer gain. A different set of switching rules, based on the evolution of the state estimates generated by the observers, is designed to orchestrate stabilizing transitions between the output feedback stability regions of the constituent modes. The differences between the state and output feedback switching strategies, and their implications for the switching logic, are discussed and a chemical process example is used to demonstrate the proposed approach. [Copyright &y& Elsevier]

Abstract: This work focuses on the development of computationally efficient predictive control algorithms for nonlinear parabolic and hyperbolic PDEs with state and control constraints arising in the context of transport-reaction processes. We first consider a diffusion-reaction process described by a nonlinear parabolic PDE and address the problem of stabilization of an unstable steady-state subject to input and state constraints. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDE, which are subsequently used for controller design. Various model predictive control (MPC) formulations are constructed on the basis of the finite dimensional approximations and are demonstrated, through simulation, to achieve the control objectives. We then consider a convection-reaction process example described by a set of hyperbolic PDEs and address the problem of stabilization of the desired steady-state subject to input and state constraints, in the presence of disturbances. An easily implementable predictive controller based on a finite dimensional approximation of the PDE obtained by the finite difference method is derived and demonstrated, via simulation, to achieve the control objective. [Copyright &y& Elsevier]

Abstract: In this work, we consider nonlinear systems with input constraints and uncertain variables, and develop a robust hybrid predictive control structure that provides a safety net for the implementation of any model predictive control (MPC) formulation, designed with or without taking uncertainty into account. The key idea is to use a Lyapunov-based bounded robust controller, for which an explicit characterization of the region of robust closed-loop stability can be obtained, to provide a stability region within which any available MPC formulation can be implemented. This is achieved by devising a set of switching laws that orchestrate switching between MPC and the bounded robust controller in a way that exploits the performance of MPC whenever possible, while using the bounded controller as a fall-back controller that can be switched in at any time to maintain robust closed-loop stability in the event that the predictive controller fails to yield a control move (due, e.g., to computational difficulties in the optimization or infeasibility) or leads to instability (due, e.g., to inappropriate penalties and/or horizon length in the objective function). The implementation and efficacy of the robust hybrid predictive control structure are demonstrated through simulations using a chemical process example. [Copyright &y& Elsevier]

In this work, a hybrid control scheme, uniting bounded control with model predictive control (MPC), is proposed for the stabilization of linear time-invariant systems with input constraints. The scheme is predicated upon the idea of switching between a model predictive controller, that minimizes a given performance objective subject to constraints, and a bounded controller, for which the region of constrained closed-loop stability is explicitly characterized. Switching laws, implemented by a logic-based supervisor that constantly monitors the plant, are derived to orchestrate the transition between the two controllers in a way that safeguards against any possible instability or infeasibility under MPC, reconciles the stability and optimality properties of both controllers, and guarantees asymptotic closed-loop stability for all initial conditions within the stability region of the bounded controller. The hybrid control scheme is shown to provide, irrespective of the chosen MPC formulation, a safety net for the practical implementation of MPC, for open-loop unstable plants, by providing a priori knowledge, through off-line computations, of a large set of initial conditions for which closed-loop stability is guaranteed. The implementation of the proposed approach is illustrated, through numerical simulations, for an exponentially unstable linear system. [Copyright &y& Elsevier]

The problem of guaranteeing stability from the entire null controllable region (NCR) for multi-input linear dynamical systems is addressed in the present manuscript. The proposed controller design is inspired by results for single input systems and generalized to multiple input systems. The approach relies on utilizing the level sets of the NCR as level sets of a Lyapunov function. A contractive constraint is incorporated into a model predictive control design, guaranteeing feasibility for any horizon length, and resulting in the NCR as the closed-loop stability region. The proposed method is illustrated using a simulation example. [ABSTRACT FROM AUTHOR]

Semicontinuous distillation is a separation technique used to purify multicomponent mixtures with low to medium throughput. This research addresses the problem of designing a Data-driven Model Predictive Control (MPC) approach that enables minimizing the Total Annualized Cost (TAC) of the semicontinuous process per tonne of feed processed while maintaining the required product purity. In lieu of typically unavailable first principles models, the manuscript demonstrates the implementation of data-driven technique using data collected from an Aspen Plus Dynamics simulation as a test bed. A subspace model identification technique is adapted to develop a multi-model framework to capture the dynamic behavior of the process and then utilized within a Shrinking Horizon MPC (SHMPC) scheme, to achieve the required objective. The simulation results demonstrate a lowering of the TAC/tonne of feed by 11.4% compared to the traditional PI setup used in the previous studies. • Data-driven Model Predictive Control (MPC) of semicontinuous process is proposed. • Multi-model approach is developed by modifying subspace identification technique. • Multiple state space models built for process with multiple modes of operations. • MPC architecture optimizes the performance of semicontinuous distillation process. • Optimization is done based on Total Annualized Cost per tonne of feed processed. [ABSTRACT FROM AUTHOR]

The present work addresses the problem of stabilizing dynamic real-time optimization and control of nonlinear systems. Dynamic real-time optimization (DRTO) is a framework for computing an optimal operating trajectory for a plant and providing corresponding set-points for an underlying control algorithm to track. This technique can be enhanced by directly modeling the underlying control algorithm (such as an MPC) in a closed-loop DRTO (CL-DRTO). This allows the CL-DRTO to predict both the controller and plant responses to set-point changes, improving performance of the overall system. Certain systems are additionally complicated by the existence of unstable steady states, which make control of the system substantially more difficult. A number of stabilizing MPC techniques have been developed in order to handle this, and a closed-loop DRTO formulation was recently proposed to handle linear systems. This work seeks to bring the improved performance of a CL-DRTO to stabilizing control systems by formulating a CL-DRTO which utilizes and explicitly models an underlying Lyapunov stabilizing MPC to achieve stabilization for nonlinear systems. The proposed formulation is compared to the previously developed formulation to demonstrate the improved closed-loop control and performance. • Novel Lyapunov-based stabilizing MPC for nonlinear plants affine in inputs. • Lyapunov constraint formulated to yield a convex MPC problem. • Dynamic RTO formulation that uses predicted response of convex Lyapunov-based MPC. • Lyapunov MPC shows improved stabilizable region over linear terminal constraint MPC. • Lyapunov MPC based DRTO shows improved economic performance for unstable plant. [ABSTRACT FROM AUTHOR]

This work presents a hybrid modeling technique that combines first-principles knowledge with principal component analysis (PCA) to detect faults in heating, ventilation, and air conditioning (HVAC) systems. Residuals, defined as the discrepancies between expected and observed behaviors, along with temperature measurements are used to develop multiple hybrid PCA models. Each model describes the normal behavior of the system in a particular operating state of air-handling units (AHUs). Hotelling's T 2 and square prediction error (SPE) statistics corresponding to the new observations are calculated using the hybrid PCA model in order to monitor the process and detect deviations from the expected behavior. The efficacy of the proposed approach to detect faults is evaluated and compared with two benchmark approaches: (1) residual analysis (based on first-principles models) and (2) a data-driven method (based on PCA) applied to raw temperature measurements. The superior performance of the proposed methodology, over the benchmarks, is shown via simulation tests with commonly occurring fault scenarios. [ABSTRACT FROM AUTHOR]

The building sector, primarily through heating, ventilation, and air conditioning (HVAC) systems, accounts for over 30% of global final energy consumption and 26% of energy-related emissions, highlighting the urgency for efficient energy management and effective fault detection. Optimizing HVAC system performance is crucial for energy conservation and sustainability. This study introduces a hybrid modeling methodology to enhance HVAC systems' fault detection and isolation (FDI). Using feature extraction through principal component analysis (PCA) and autoencoder (AE), the proposed approach integrates first-principles knowledge with data to improve the performance of different clustering algorithms (K-means, density-based spatial clustering of applications with noise (DBSCAN), and ordering points to identify the clustering structure (OPTICS)) to distinguish datasets of different operating conditions (normal and faulty conditions). The proposed approach is applied to detect common faults in HVAC systems, demonstrating superior performance compared to purely data-driven methods. • A hybrid clustering approach is proposed by integrating first-principles knowledge with data. • This approach is utilized to distinguish data samples related to normal and faulty operating conditions in HVAC systems. • The superior performance of the hybrid approach compared to a purely data-driven approach is shown. [ABSTRACT FROM AUTHOR]

[Display omitted] • Demonstrates efficacy of simple linear dynamic models to model hydroprocessing units. • Suggests practitioners to use the right tool depending on the type of data. • Subspace identification for missing data approach generalized to handle missing data in inputs both during training and validation. Hydroprocessing units in petroleum refineries comprise of several complex interconnected network of unit operations, and perform the function of removing impurities from the crude, and cracking it to lighter products for subsequent operations. Modeling these units play a pivotal role in predicting future values of important variables, improving the control and optimization of the plant for efficient operation among several other applications. This paper presents the development and implementation of data-based models in estimating product qualities and other key monitoring variables in the hydroprocessing unit of an industrial refinery. Real industrial data from two different units was used and appropriate data-driven modeling strategies were formulated in order to address this problem. In one instance, the usefulness of Dynamic-Partial Least Squares (DPLS) over Partial Least Squares (PLS) in the estimation of important variables of the unit is demonstrated. In the other instance, subspace identification methodology is found to yield a superior model. The methods used in this study can also elegantly handle the missing data problem associated with real data sets, and thus demonstrate the ability, and the importance of using the right data driven technique for specific problems in the context of hydroprocessing refinery. [ABSTRACT FROM AUTHOR]

The present manuscript addresses the problem of designing a procedure for constructing the null controllable Region (the set of states from where stabilization is achievable subject to constraints). To this end, a controllability minimum principle based computationally tractable approach for constructing the null controllable region is presented. Simulation results are used to illustrate the computation of the NCR for several examples. [ABSTRACT FROM AUTHOR]

The manuscript addresses the problem of developing a modelling strategy that can accurately capture the dynamics of a non-linear batch process, demonstrated on a uni-axial rotational molding process. To this end, this work presents a strategy that utilizes the nonlinear modeling capability of a recurrent neural network (RNN) model, while leveraging the rigor of subspace-based approaches. The proposed modeling strategy is as follows: a subspace identification algorithm is first utilized on all the training batches to obtain the state sequence for these batches. Then the state sequences from all the training batches and the corresponding input sequences are given as the output and the input to a neural network, respectively. This step essentially builds a non-linear state space model, albeit using the state trajectory identified by the subspace model. The output equation obtained from the above-mentioned subspace identification step along with the newly obtained non-linear state equation is now ready to be used for predicting the output trajectory of the system. The results from the experiments illustrate the improved prediction performance of a model obtained by the proposed hybrid Subspace-RNN approach in comparison to both a subspace-based approach and an RNN-based approach. • The manuscript addresses the problem of control of quality variables for batch processes while handling process nonlinearity. • Nonlinearity is handled by using neural networks, but in a way that avoids overfitting. • Use of underlying state trajectories identified by the more robust, but linear, subspace identification method. • The proposed method leads to better prediction accuracy than linear models and an RNN model by themselves. [ABSTRACT FROM AUTHOR]

This manuscript addresses the problem of data driven model based economic model predictive control (MPC) design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example. [ABSTRACT FROM AUTHOR]