NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.Peer ReviewedPostprint (author's final draft

A Parametric Non-Convex Decomposition Algorithm for Real-Time and Distributed NMPC

A novel decomposition scheme to solve parametric non-convex programs as they arise in Nonlinear Model Predictive Control (NMPC) is presented. It consists of a fixed number of alternating proximal gradient steps and a dual update per time step. Hence, the proposed approach is attractive in a real-time distributed context. Assuming that the Nonlinear Program (NLP) is semi-algebraic and that its critical points are strongly regular, contraction of the sequence of primal-dual iterates is proven, implying stability of the sub-optimality error, under some mild assumptions. Moreover, it is shown that the performance of the optimality-tracking scheme can be enhanced via a continuation technique. The efficacy of the proposed decomposition method is demonstrated by solving a centralised NMPC problem to control a DC motor and a distributed NMPC program for collaborative tracking of unicycles, both within a real-time framework. Furthermore, an analysis of the sub-optimality error as a function of the sampling period is proposed given a fixed computational power.Comment: 16 pages, 9 figure

Model predictive torque vectoring control with active trail-braking for electric vehicles

In this work we present the development of a torque vectoring controller for electric vehicles. The proposed controller distributes drive/brake torque between the four wheels to achieve the desired handling response and, in addition, intervenes in the longitudinal dynamics in cases where the turning radius demand is infeasible at the speed at which the vehicle is traveling. The proposed controller is designed in both the Linear and Nonlinear Model Predictive Control framework, which have shown great promise for real time implementation the last decades. Hence, we compare both controllers and observe their ability to behave under critical nonlinearities of the vehicle dynamics in limit handling conditions and constraints from the actuators and tyre-road interaction. We implement the controllers in a realistic, high fidelity simulation environment to demonstrate their performance using CarMaker and Simulink

Hybrid nonlinear model predictive control of a cooling water network

A Hybrid Nonlinear Model Predictive Control (HNMPC) strategy is developed for temperature control and power consumption minimisation of a cooling water network. The HNMPC uses a gradient descent optimisation algorithm for the continuous manipulated variables, and an enumerated tree traversal algorithm to control and optimise the Boolean manipulated variables. The HNMPC is subjected to disturbances similar to those experienced on a real plant, and its performance compared to a continuous Nonlinear Model Predictive Control (NMPC) and two base case scenarios. Power consumption is minimised, and process temperature disturbances are successfully rejected. Monetary benefits of the HNMPC control strategy are estimated.The National Research Foundation of South Africahttp://www.elsevier.com/locate/conengprac2021-04-01hj2020Electrical, Electronic and Computer Engineerin

Data-driven Economic NMPC using Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on the behavior of the resulting control scheme. In contrast, Nonlinear Model Predictive Control (NMPC) and Economic NMPC (ENMPC) are standard tools for the closed-loop optimal control of complex systems with constraints and limitations, and benefit from a rich theory to assess their closed-loop behavior. Unfortunately, the performance of (E)NMPC hinges on the quality of the model underlying the control scheme. In this paper, we show that an (E)NMPC scheme can be tuned to deliver the optimal policy of the real system even when using a wrong model. This result also holds for real systems having stochastic dynamics. This entails that ENMPC can be used as a new type of function approximator within RL. Furthermore, we investigate our results in the context of ENMPC and formally connect them to the concept of dissipativity, which is central for the ENMPC stability. Finally, we detail how these results can be used to deploy classic RL tools for tuning (E)NMPC schemes. We apply these tools on both a classical linear MPC setting and a standard nonlinear example from the ENMPC literature

Model-based approach for the plant-wide economic control of fluid catalytic cracking unit

Fluid catalytic cracking (FCC) is one of the most important processes in the petroleum refining industry for the conversion of heavy gasoil to gasoline and diesel. Furthermore, valuable gases such as ethylene, propylene and isobutylene are produced. The performance of the FCC units plays a major role on the overall economics of refinery plants. Any improvement in operation or control of FCC units will result in dramatic economic benefits. Present studies are concerned with the general behaviour of the industrial FCC plant, and have dealt with the modelling of the FCC units, which are very useful in elucidating the main characteristics of these systems for better design, operation, and control. Traditional control theory is no longer suitable for the increasingly sophisticated operating conditions and product specifications of the FCC unit. Due to the large economic benefits, these trends make the process control more challenging. There is now strong demand for advanced control strategies with higher quality to meet the challenges imposed by the growing technological and market competition. According to these highlights, the thesis objectives were to develop a new mathematical model for the FCC process, which was used to study the dynamic behaviour of the process and to demonstrate the benefits of the advanced control (particularly Model Predictive Control based on the nonlinear process model) for the FCC unit. The model describes the seven main sections of the entire FCC unit: (1) the feed and preheating system, (2) reactor, (3) regenerator, (4) air blower, (5) wet gas compressor, (6) catalyst circulation lines and (7) main fractionators. The novelty of the developed model consists in that besides the complex dynamics of the reactorregenerator system, it includes the dynamic model of the fractionator, as well as a new five lump kinetic model for the riser, which incorporates the temperature effect on the reaction kinetics; hence, it is able to predict the final production rate of the main products (gasoline and diesel), and can be used to analyze the effect of changing process conditions on the product distribution. The FCC unit model has been developed incorporating the temperature effect on reactor kinetics reference construction and operation data from an industrial unit. The resulting global model of the FCC unit is described by a complex system of partial-differential-equations, which was solved by discretising the kinetic models in the riser and regenerator on a fixed grid along the height of the units, using finite differences. The resulting model is a high order DAE, with 942 ODEs (142 from material and energy balances and 800 resulting from the discretisation of the kinetic models). The model offers the possibility of investigating the way that advanced control strategies can be implemented, while also ensuring that the operation of the unit is environmentally safe. All the investigated disturbances showed considerable influence on the products composition. Taking into account the very high volume production of an industrial FCC unit, these disturbances can have a significant economic impact. The fresh feed coke formation factor is one of the most important disturbances analysed. It shows significant effect on the process variables. The objective regarding the control of the unit has to consider not only to improve productivity by increasing the reaction temperature, but also to assure that the operation of the unit is environmentally safe, by keeping the concentration of CO in the stack gas below a certain limit. The model was used to investigate different control input-output pairing using classical controllability analysis based on relative gain array (RGA). Several multi-loop control schemes were first investigated by implementing advanced PID control using anti-windup. A tuning approach for the simultaneous tuning of multiple interacting PID controllers was proposed using a genetic algorithm based nonlinear optimisation approach. Linear model predictive control (LMPC) was investigated as a potential multi-variate control scheme applicable for the FCCU, using classical square as well as novel non-square control structures. The analysis of the LMPC control performance highlighted that although the multivariate nature of the MPC approach using manipulated and controlled outputs which satisfy controllability criteria based on RGA analysis can enhance the control performance, by decreasing the coupling between the individual low level control loops operated by the higher level MPC. However the limitations of using the linear model in the MPC scheme were also highlighted and hence a nonlinear model based predictive control scheme was developed and evaluated.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Integration of anti-lock braking system and regenerative braking for hybrid/electric vehicles

Vehicle electrification aims at improving energy efficiency and reducing pollutant emissions which creates an opportunity to use the electric machines (EM) as Regenerative Braking System (RBS) to support the friction brake system. Anti-lock Braking System (ABS) is part of the active safety systems that help drivers to stop safely during panic braking while ensuring the vehicle’s stability and steerability. Nevertheless, the RBS is deactivated at a safe (low) deceleration threshold in favour of ABS. This safety margin results in significantly less energy recuperation than what would be possible if both RBS and ABS were able to operate simultaneously. Vehicle energy efficiency can be improved by integrating RBS and friction brakes to enable more frequent energy recuperation activations, especially during high deceleration demands. The main aim of this doctoral research is to design and implement new wheel slip control with torque blending strategies for various vehicle topologies using four, two and one EM. The integration between the two braking actuators will improve the braking performance and energy efficiency of the vehicle. It also enables ABS by pure EM in certain situations where the regenerative brake torque is sufficient. A novelmethod for integrating the wheel slip control and torque blending is developed using Nonlinear Model Predictive Control (NMPC). The method is well known for the optimal performance and enforcement of critical control and state constraints. A linear MPC strategy is also developed for comparison purpose. A pragmatic brake torque blending algorithm using Daisy-Chain with sliding mode slip control is also developed based on a pre-defined energy recuperation priority. Simulation using high fidelity model using co-simulation in Matlab/Simulink and CarMaker is used to validate the developed strategies. Different test patterns are used to evaluate the controllers’ performance which includes longitudinal and lateral motions of the vehicle. Comparison analysis is done for the proposed strategies for each case. The capability for real-time implementation of the MPC controllers is assessed in simulation testing using dSPACE hardware