PROCESS/MODEL MISMATCH COMPENSATION FOR MODEL-BASED CONTROLLERS

The control performance and robust behaviour of model-based control algorithms have been proved better than those of the traditional control algorithms. However, when the processes have highly nonlinear behaviour, use of linear model-based control strategies alone does not quite provide good control performance. Therefore, in this study a nonlinear-state feedback control (NSFC) strategy is developed to overcome such nonlinear process control problems. The basic structure of the NSFC strategy is coupled with a steady-state model and a nonlinear-state feedback controller derived from the nonlinear process model. We then apply the NSFC concept to establish an nonlinear inferential control system to control the packed-bed reactor. Simulation results have shown that the control performances of the NSFC scheme are much better than those of the internal model control (IMC) scheme and the generic model control (GMC) scheme.

GMC-fuzzy control of pH during enzymatic hydrolysis of cheese whey proteins

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Non-linear adaptive control of fermentation processes utilizing a priori modelling knowledge

The study of an optimal cooling control strategy for a batch white sugar crystallization process is reported in this paper. Within the proposed control strategy, a dynamic optimization algorithm (DOA) and a generic model control (GMC) algorithm were compared with respect to the final process quality achieved. The linear models required in the controller structures were extracted applying two identification alternatives. The GMC algorithm cases seem to guarantee more satisfactory end point quality of the process. However, only the DOA of the supersaturation manipulating the steam flowrate makes feasible all conflicting control objectives.

This paper proposes a novel control scheme for hybrid energy storage system(HESS) in electric vehicles. The HESS above is composed of two parts: fuel cell (FC) and super capacitor(SC). FC, as the main power supply, provides a large proportion of required power. Whereas, SC, as the auxiliary energy storage source, plays a great role when starting and braking an electric vehicle(EV). Both energy sources are connected to the DC bus with DC-DC converters. Two converters share the very DC bus which is connected to the traction motor with a power inverter. To make sure the performances of the control system, a control method named generic model control is applied. Simulation results indicate the robustness and static performance of the proposed control scheme.

Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states [1]. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained non-linear optimization [2-12]. In this work, we present an alternative approach to control batteries applying the concept of differential geometry. This approach is known as Generic Model Control (GMC), or Reference System Synthesis [13,14]. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models. This work builds on our previous work of implementation of GMC for differential variables occurring in a model consisting of differential algebraic equations (DAEs), and extends it to set point based control for algebraic variables. Conventional GMC is applicable for cases in which the output is typically independent of control [13]. We also extend it for cases for which the measured variable can be an explicit function of the manipulated variable. As compared to the generic model control approaches implemented by previous researchers, we have implemented the same concept using direct DAE numerical solvers [15]. The results are presented for single input single objective, and for constrained problems. The proposed approach is compared with standard control approaches and is found to be equally robust if not better. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 and the Clean Energy Institute at the University of Washington, Seattle.

On-line dynamic optimization integrated with generic model control of a batch crystallizer

To be implemented within Generic Model Control, a process model must have a relative order of one. When systems have relative orders greater than one (“high relative order systems”) techniques are ...

On-line optimization and control of fed-batch fermentation is difficult due to its non linearity and dynamic operation points with uncertainty. A novel cascade optimal control algorithm was proposed to treat these difficulties with the operating points, which maximize the objective function, was defined by Necessary Conditions of Optimization (NCO). Consequently, a nonlinear Generic Model Control (GMC) was applied to eliminate effect of uncertainty. Calculations of Hamilton gradient online and repeated optimization were avoided. The simulation on saccharomyces cerevisiae enzyme cultivation process showed the efficiency and advantage of the proposed method.

This paper presents a new method for multivariable dead-time compensation for a wide class of chemical engineering process control problems. The method utilizes the powerful performance-following property of Generic Model Control (GMC). The design procedure involves model selection including process deadtime estimates together with a trajectory specification. A subset of the method is derived for use of nonlinear steady-state models directly in the control algorithm. Simulation of both linear and nonlinear systems demonstrate the simplicity and efficiency of the technique.

This paper presents an attractive image-based visual servoing approach for eye-in-hand robotic systems based on new binocular stereo vision servoing model. The major contribution of this work is in devising an optimization controller to control the velocity of the camera and GMC(Generic Model Control) is introduced to confirm the output trajectory of the system. The scheme does not deed to know the geometry model of the system. By selecting proper parameters to adjust transient time, the time for the output to follow prearranged track, is adjusted. Asymptotic convergence of the image errors to zero is proved by Lyapunov theory. Simulation results are presented for 6 DOF eye-in-hand system, which clearly show the validity of the proposed approach.

Molecular weight is one of the influential attributes in a polymerization process and its control in semi-batch operation poses considerable difficulty due to complexities of the process. The present study focuses on the development of different control strategies for controlling the molecular weight by tracking reactor temperature and composition control using jacket temperature and feed flow rate as manipulated variables. The copolymerization of styrene acrylonitrile with xylene as solvent and AIBN as initiator is considered for the present study. The control strategies evaluated are Multi-loop-proportional integral (PI) control, multivariable-PI control and nonlinear generic model control (GMC). The present study showed that control of temperature and composition has resulted in achieving the desired molecular weight. Nonlinear GMC control is able achieve targeted molecular weight closely even in the presence of unmeasured load disturbances in the monomer feed concentration and initial initiator concentration, whereas multi-loop and multivariable PI controllers resulted in some offset.

Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases