Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling

Constrained nonlinear multivariable control of a fluid catalytic cracking process

Underwater glider platform represents the maturing technology with a large cost saving over current underwater sampling process. It can survey and monitor the sea environment cost-effective manner combining survey capabilities, simultaneous water sampling and environmental data gathering capacities. It can perform a wide range of fully automated monitoring data measurement over an extended period of time. This paper will focus on the design of multivariable underactuated nonlinear adaptive control using U-model methodologies. Underwater glider control, modelling and identification approach was reviewed in order to formulate the design, development and control approach of underwater glider development using multivariable adaptive U-model nonlinear control approach. U-model methodology simplifies the control synthesis with the influence of the uncertainties and external disturbances by selecting appropriate control structures. Most of the autonomous underwater vehicle (AUV) neglected the coupling effect of the dynamics during process modelling while U-model enables to include the coupling effect using the inverse Jacobian matrix. Thus contributes towards the underactuated nonlinear adaptive control development and process modelling.

COMPUTER-AIDED DESIGN OF MULTIVARIABLE STOCHASTIC CONTROL SYSTEMS WITH A-PRIORI PRESCRIBED DYNAMICAL PROPERTIES

As the flatness control system is a multi-variable control system, the key issue for high-precision flatness is the determination of the optimal adjustments of flatness actuators. In order to determine these the first step is the establishment of a multi-variable control model in the flatness control process, by which the actual flatness control problem has been reduced to a non-linear optimisation problem with box-constraints. Around this optimisation problem, characteristics and applicability of current optimisation algorithms were analysed. Based on the coordinate descent method, a new multi-variable optimisation algorithm with global convergence was proposed. In the algorithm, the problem was transformed into a series of univariate optimisations which could be solved by sequentially searching along coordinate directions. In order to make the objective function decrease rapidly, and ensure that each iteration are carried out within the feasible region, the accelerating step method is adopted to determine the iterative step size. A series of feasible points have been obtained through a series of iterations, which make the objective function decrease gradually until the optimal solution has been obtained. Finally, the algorithm has been tested by numerical experiments with production data of actual flatness control process, and applied to the 1450 mm tandem cold mill. Numerical experiments and application show that the proposed algorithm not only can satisfy the requirements of response speed, but also have a good accuracy, which can provide a reference for the realisation of high-precision flatness control processes of cold rolled strip.

In this chapter, a non-linear constrained optimization strategy is proposed and applied to a fluid catalytic cracking reactor-regenerator section. A dynamic parameter update algorithm was developed and used to reduce the effect of larger modelling errors by regularly updating the selected model parameters. This algorithm is capable of adapting the model parameters in a non-linear model. This chapter also presents and discusses the dynamic process models identified from open-loop step test data. The constrained non-linear optimization algorithm and strategies were tested in real-time on a fluid catalytic cracking reactor-regenerator section and the results were compared to linear multivariable controller such as dynamic matrix control (DMC).KeywordsFeed Flow RateFluid Catalytic CrackManipulate VariableDynamic Matrix ControlFluid Catalytic Crack UnitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A detailed analysis of a number of multivariable process control strategies, including non-interacting control, optimal control, inverse Nyquist array and characteristic locus techniques, is carried out theoretically on a one-dimensional, two-input heat conduction system. Additionally, a new non-interacting control strategy, termed inner-loop decoupling, is developed. The potential improvements in control system performance through the use of extra measurements and through the appropriate selection of measurement locations is assessed. Results show the advantages and disadvantages of the techniques, point to the flexibility of the characteristic locus method and indicate the effectiveness of the new method of inner-loop decoupling.

The control problem of fluid catalytic cracking (FCC) units is a challenging task due to its model complexity, on linear dynamics, constrained variables and cross coupling interaction between inputs and outputs. Mathematical Control of FCC is challenging and there is a strong incentive to use multivariable control schemes. In this paper, first principle Model of FCCU is developed after analysis of dynamic behavior of this complex process which was sufficiently complex to capture the major dynamic effects that occur in an actual system. Difficulties caused by the interactions are always encountered in the design of multi-loop control systems for MIMO processes like FCCU.The investigated control approach is the multivariable PID which uses BLT tuning. In this detuning method, each controller in the system is designed based on the corresponding diagonal element and ignore the interactions from other loops. The controllers are then detuned to take into accounts the interactions until some prescribed limit (e.g. the biggest log-modulus) is attained.

The paper proposes a combined feedforward and feedback control scheme with nonlinear compensators as a solution for multivariable nonlinear processes. This is developed based on classic decentraliz...

Multivariable active disturbance rejection control for compression liquid chiller system

Air-Handling or Air-handler Units (AHUs) as a section of a Heating, Ventilating, and Air-Conditioning (HVAC) system are used for refrigerating and filtering the environment. AHU provides comfortable conditions by adjusting the control actuators of the air and water in a field-level of industrial automation. This device has strong interacting nonlinearities characteristics with complicated coupling between states and variable parameters. Thus, designing a controller has been always a challenging issue. In this work, a multivariable Nonlinear Model Predictive Controller (NMPC) centered on fmincon algorithm for AHU with a single zone in a Variable Air Volume (VAV) is pro-posed. The NMPC structure is focused on iterative optimization (online) of a cost function with a finite horizon of operating performance and using the future behavior of air-handler to generate the control effort without considering the past error. Simulation results indicate good efficiency of the NMPC approach in estimation and tracking the desired set-point compared to Linear Quadratic Regulator (LQR).

In this chapter an adaptive sliding mode controller for the Furuta pendulum is proposed. The Furuta pendulum is a class of underactuated mechanical systems commonly used in many control systems laboratories due to its complex stabilization which allows the analysis and design of different nonlinear and multivariable controllers that are useful in some fields such as aerospace and robotics. Sliding mode control has been extensively used in the control of mechanical systems as an alternative to other nonlinear control strategies such as backstepping, passivity based control etc. The design and implementation of an adaptive sliding mode controller for this kind of system is explained in this chapter, along with other sliding mode controller variations such as second order sliding mode (SOSMC) and PD plus sliding mode control (PD \(+\) SMC) in order to compare their performance under different system conditions. These control techniques are developed using the Lyapunov stability theorem and the variable structure design procedure to obtain asymptotically stable system trajectories. In this chapter the adaptive sliding mode consist of a sliding mode control law with an adaptive gain that makes the controller more flexible and reliable than other sliding mode control (SMC) algorithms and nonlinear control strategies. The adaptive sliding mode control (ASMC) of the Furuta pendulum, and the other SMC strategies shown in this chapter, are derived according to the Furuta’s pendulum dynamic equations making the sliding variables, position errors and velocity errors reach the zero value in a specified reaching time. The main reason of deriving two well known sliding mode control strategy apart from the proposed control strategy of this chapter (adaptive sliding mode control) is for comparison purposes and to evince the advantages and disadvantages of adaptive sliding mode control over other sliding mode control strategies for the stabilization of the Furuta pendulum. A chattering analysis of the three SMC variations is done, to examine the response of the system, and to test the performance of the ASMC in comparison with the other control strategies explained in this chapter.

This rigorous yet accessible textbook provides broad and systematic coverage of linear multivariable control systems, including several new approaches to design. In addition to standard state space theory, it provides a new measurement-based approach to linear systems, including a generalization of Thevenin's Theorem, a new single-input single-output approach to multivariable control, and analytical design of PID controllers developed by the authors. Each result is rigorously proved and combined with specific control systems applications, such as the servomechanism problem, the fragility of high order controllers, multivariable control, and PID controllers. Illustrative examples solved using MATLAB and SIMULINK, with easily reusable programming scripts, are included throughout. Numerous end-of-chapter homework problems enhance understanding. Based on course-tested material, this textbook is ideal for a single or two-semester graduate course on linear multivariable control systems in aerospace, chemical, electrical and mechanical engineering.

This rigorous yet accessible textbook provides broad and systematic coverage of linear multivariable control systems, including several new approaches to design. In addition to standard state space theory, it provides a new measurement-based approach to linear systems, including a generalization of Thevenin's Theorem, a new single-input single-output approach to multivariable control, and analytical design of PID controllers developed by the authors. Each result is rigorously proved and combined with specific control systems applications, such as the servomechanism problem, the fragility of high order controllers, multivariable control, and PID controllers. Illustrative examples solved using MATLAB and SIMULINK, with easily reusable programming scripts, are included throughout. Numerous end-of-chapter homework problems enhance understanding. Based on course-tested material, this textbook is ideal for a single or two-semester graduate course on linear multivariable control systems in aerospace, chemical, electrical and mechanical engineering.

A synthetical method of multivariable control system performance assessment is proposed in this paper, which uses multivariable minimum variance control (MVC) benchmark to determine the stochastic performance, and normalized multivariate impulse response (NMIR) curve as an alternative measure of performance to test the dynamic performance, and with the help of auto-correlation function (ACF) and cross-correlation function (CCF) to analyse if there are oscillations exist. The method is applied to assess the performance of multivariable predictive control system of industrial distillation column.

This rigorous yet accessible textbook provides broad and systematic coverage of linear multivariable control systems, including several new approaches to design. In addition to standard state space theory, it provides a new measurement-based approach to linear systems, including a generalization of Thevenin's Theorem, a new single-input single-output approach to multivariable control, and analytical design of PID controllers developed by the authors. Each result is rigorously proved and combined with specific control systems applications, such as the servomechanism problem, the fragility of high order controllers, multivariable control, and PID controllers. Illustrative examples solved using MATLAB and SIMULINK, with easily reusable programming scripts, are included throughout. Numerous end-of-chapter homework problems enhance understanding. Based on course-tested material, this textbook is ideal for a single or two-semester graduate course on linear multivariable control systems in aerospace, chemical, electrical and mechanical engineering.