This book provides a unified, practically-oriented treatment to many constrained control paradigms. Recently proposed control strategies are unified in a generalised framework to deal with different kinds of constraints. The book's solutions are based on reference conditioning ideas implemented by means of supervisory loops, and they are complementary to any other control technique used for the main control loop. Although design simplicity is a book priority, the use of well established sliding mode concepts for theoretical analysis make it also rigorous and self-contained. The first part of the book focuses on providing a simple description of the method to deal with system constraints in SISO systems. It also illustrates the design and implementation of the developed techniques through several case studies. The second part is devoted to multivariable constrained control problems: improving system decoupling under different plant or controller constraints, and reducing the undesired effects caused by manual-automatic or controller switching. The key aim of this book is to reduce the gap between the available constrained control literature and industrial applications.
Inspec keywords: multivariable control systems; variable structure systems; process control; design engineering
Other keywords: industrial application; control loop; SISO system; multivariable constrained control problem; advanced control strategy; system decoupling; supervisory loops; controller constraint; design simplicity; sliding mode concept; constrained control paradigm; constrained process control technique; controller switching; manual-automatic switching
Subjects: Control technology and theory (production); Industrial processes; Design; Control in industrial production systems; Multivariable control systems
Types of constraints that have been dealt with to a much lesser extent but that also have an effect on the achievable performance. There also exist structural or dynamic constraints that together with performance specifications, environmental regulations or safety rules usually require system states or outputs to be bounded. Although constrained control problems have been studied primarily in SISO systems, the majority of the real-world processes have more than one variable to be controlled and possess more than one control action for this objective. These systems are called multivariable systems or multiple-input multiple-output (MIMO) systems.
This chapter presents the main ideas of the book to deal with constraints in feed back control systems. The proposed method aims at preserving the simplicity of conventional anti-windup (AW) algorithms, but at the same time it is supported by a solid theoretical background and exhibits distinctive robustness features. Among them, the method does not require an exact model of the plant (it only needs knowledge of the model structure), it can be employed to address constraints in non-linear processes and it can transparently deal with a wide variety of non linearities apart from amplitude saturation, such as rate limiter, asymmetry, dead zone, and time dependency. For the sake of simplicity, only single-input single output (SISO) systems are considered in this chapter.
In this chapter, the practical potentials of the sliding mode reference conditioning (SMRC) approach to deal with different kinds of constraints are illustrated with four case studies: (1) the pitch control of wind turbines with both amplitude and rate actuator saturation; (2) a clean hydrogen production system with structural constraints in which the electrolyser specifications require output bounds; (3) the tracking speed autoregulation of robotic manipulators in order to avoid path deviations; and (4) the regulation of ethanol concentration below a given threshold in the fed batch fermentation of an industrial strain for overflow metabolism avoidance.
This chapter presents multivariable dynamical system and improvement in the decoupling of multiple-input multiple-output (MIMO) systems in the presence of different types of constraints, that is, to reduce interactions among the loops as much as possible when the plant under control is subjected to either physical, structural or dynamic constraints. First, we recall some basic concepts of MIMO systems analysis. Then, the affine parameterisation of all the controllers which internally stabilise the feedback system is introduced, together with the closely related Internal Model Control (IMC). Based on this control strategy, some design procedures are described in order to achieve the dynamic decoupling of stable minimum phase (MP) systems, non-minimum phase (NMP) systems and unstable systems. Finally, the effects and limitations of diagonal (full) dynamic decoupling in systems with right-half plane (RHP) zeros are briefly discussed.
In Chapter 4, we described a methodology to achieve the closed-loop dynamic decoupling of multivariable systems. Although some difficulties arising from right half plane (RHP) zeros were pointed out, the decoupling design implicitly assumed unconstrained systems and centralised controllers, as in general do the great majority of the existing techniques. Hereinafter (Chapters 5-7), we will take advantage of the sliding mode reference conditioning (SMRC) features to improve the closed-loop decoupling in the presence of either physical (actuator saturations), structural (decentralised controllers) or dynamic (non-minimum phase (NMP) plants) limitations. When the unavoidable physical limits of the real actuators are taken into account, the activation of any of them produces a change in the direction of the plant input with respect to the controller output and, as a consequence, the decoupling obtained for linear operation is lost. In this chapter, we first illustrate the control directionality problem briefly introduced in Chapter 1, and we then present a compensation method using SMRC ideas to preserve the closed-loop dynamic decoupling in presence of input constraints.
The methods for reducing or cancelling crossed interactions studied so far in multivariable systems were based on centralised multiple-input multiple-output (MIMO) controllers, which is also usual in multivariable control literature. How ever, despite the performance advantages of centralised controllers, the great majority of industrial process control applications still rely on decentralised or multiloop control structures. Because of their structural constraints, decentralised controllers are not able to suppress by their own interactions of the plant, which are only taken into account at the controller tuning phase. This is not a trivial problem to be solved. In fact, even when supervisory control tools like model predictive control are employed, the coupling among the loops has to be addressed at the lower level (generally decentralised PI/PID control) because of the long sampling time of the supervisory modes. Therefore, the coupling reduction under decentralised structures is a topic of great interest when considering practical control issues. In this chapter, we shall analyse and address this problem. First, some basic concepts related with this control topology are presented. In particular, the relative gain array (RGA) is introduced as a simple interaction measure, whereas the potential effects of crossed interactions are illustrated through a simple example. Then, the sliding mode reference conditioning (SMRC) technique is exploited here in order to limit the amplitude of decentralised control interactions.
Triangular decoupling has been considered in several contributions as an alternative approach to relax the dynamic restrictions imposed by diagonal decoupling in non minimum phase (NMP) multivariable systems. This strategy allows decoupling a given variable without transferring to its response NMP characteristics. Additionally, since it is based on a centralised architecture, the achievable closed-loop performance is clearly superior to that of decentralised control, particularly for NMP plants. However, when right-half plane (RHP) zeros are aligned with the variable of interest (the decoupled variable), the aforementioned advantages of partial decoupling come at the cost of large interactions in the remaining system variables. In this chapter, we first study the reasons of this peculiar feature of partially decoupled NMP systems and then employ sliding mode reference conditioning (SMRC) ideas to delimit the remaining interactions without producing inverse responses in the main variable.
This chapter describes the undesired effects caused by manual-automatic or con troller switching in process control. Manual-automatic commutations are often used in the start-up schemes of the process industry, whereas switching among different controllers is frequently used for the control of non-linear systems at given operating points. Both types of switching at the plant input can be attributed to structural constraints, limiting either the controller type (linear controllers are typically preferred) or the switching policy (industry applications seldom include sophisticated approaches, like linear parameter-varying). The performance degradation caused by these mode switches is first tackled by means of sliding mode reference conditioning (SMRC) ideas in single-input single output (SISO) systems. Then, multivariable sliding mode (SM) concepts are introduced and the SMRC bumpless algorithm is extended to deal with multiple input multiple-output (MIMO) processes, so as to avoid inconsistencies between the off-line controller outputs and the plant inputs. As a consequence, jumps at the plant inputs are prevented and undesired transients on controlled variables are significantly reduced.