Virtual reference feedback tuning: a direct method for the design of feedback controllers

This paper considers the problem of designing a controller for an unknown plant based on input/output measurements. The new design method we propose is direct (no model identification of the plant is needed) and can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the case of restricted complexity controller design, the achieved controller is a good approximation of the restricted complexity global optimal controller. A simulation example shows the effectiveness of the method.     

An alternative structure for next generation regulatory controllers. Part II: Stability analysis,tuning rules and experimental validation

We recently developed, as an alternative to the PID controller, a novel 4-mode control scheme that takes full advantage of modern electronic hardware components, and whose tuning parameters are directly related to controller performance attributes (robustness, set-point tracking, and disturbance rejection); its achievable performance is better than that of the PID controller, and it can be designed and implemented much more directly and transparently. In this companion paper we present robust stability results for the proposed controller, for any given plant/model mismatch; these results are then used to generate simple tuning rules for the controller. The effect of noise on controller performance is explicitly considered and modified tuning rules are proposed for excessively noisy processes. The controller tuning results and procedure are then illustrated via experimental testing and validation. First, water level control in a simple laboratory scale process is used to illustrate the design, tuning, and implementation of the RTDA controller. We use this process primarily to evaluate the controller performance and illustrate how the tuning parameters influence, directly and independently, the controller performance attributes. The controller is then implemented on a pilot-scale physical vapor deposition process to demonstrate its performance on a more complicated process that is prototypical of 21st century manufacturing. We demonstrate that the proposed controller achieves improved performance with minimal tuning effort.     

A receding horizon control approach to sampled-data implementation of continuous-time controllers

We propose a novel way for sampled-data implementation (with the zero order hold assumption) of continuous-time controllers for general nonlinear systems. We assume that a continuous-time controller has been designed so that the continuous-time closed-loop satisfies all performance requirements. Then, we use this control law indirectly to compute numerically a sampled-data controller. Our approach exploits a model predictive control (MPC) strategy that minimizes the mismatch between the solutions of the sampled-data model and the continuous-time closed-loop model. We propose a control law and present conditions under which stability and sub-optimality of the closed loop can be proved. We only consider the case of unconstrained MPC. We show that the recent results in [G. Grimm, M.J. Messina, A.R. Teel, S. Tuna, Model predictive control: for want of a local control Lyapunov function, all is not lost, IEEE Trans. Automat. Control 2004, to appear] can be directly used for analysis of stability of our closed-loop system.     

Analytical method of PID controller design for parallel cascade control

An analytical method of PID controller design is proposed for parallel cascade control. Firstly, a general structure for parallel cascade control is proposed that takes both setpoint and load disturbance responses into account. Analytical tuning rules for the PID controllers are then derived for the general process model by employing the IMC design procedure. The proposed method offers a simple and effective way to obtain the PID controller rules for parallel cascade control system which takes into account the interaction between primary and secondary control loops. The simulation results illustrate the application of the proposed method and demonstrate its superiority compared to several alternatives.     

Extensions to “virtual reference feedback tuning: A direct method for the design of feedback controllers”

The papers [Campi, Lecchini & Savaresi (2002). Automatica, 38(8), 1337-1346; (2003). European Journal of Control, 9(1), 66-76] present a direct controller synthesis procedure that uses identification algorithms applied to filtered input-output plant data. This contribution discusses variations that, in some cases, may alleviate noise-induced correlation (in the open-loop case) and allow the applicability of the approach to unstable plants. Importantly, it also introduces an invalidation test step based on the available data (i.e., prior to experimental controller testing), to check if the flexibility of the controller parameterisation and the approximations involved are suitable for the design objectives or, on the contrary, the resulting closed loop may be unstable.     

A model-free design of reduced-order controllers and application to a DC servomotor

This paper presents a new model-free technique to design fixed-structure controllers for linear unknown systems. In the current control design approaches, measured data are used to first identify a model of the plant, then a controller is designed based on the identified model. Due to errors associated with the identification process, degradation in the controller performance is expected. Hence, we use the measured data to directly design the controller without the need for model identification. Our objective here is to design measurement-based controllers for stable and unstable systems, even when the closed-loop architecture is unknown. This proposed method can be very useful for many industrial applications. The proposed control methodology is a reference model design approach which aims at finding suitable parameter values of a fixed-order controller so that the closed-loop frequency response matches a desired frequency response. This reference model design problem is formulated as a nonlinear programming problem using the concept of bounded error, which can then be solved to find suitable values of the controller parameters. In addition to the well-known advantages of data-based control methods, the main features of our proposed approach are: (1) the error is guaranteed to be bounded, (2) it enables us to avoid issues related to the use of minimization methods, (3) it can be applied to stable and unstable plants and does not require any knowledge about the closed-loop architecture, and (4) the controller structure can be selected a priori, which means that low-order controllers can be designed. The proposed technique is experimentally validated through a real position control problem of a DC servomotor, where the results demonstrate the efficacy of the proposed method.     

A three-loop model-following control structure: theory and implementation

This article presents a novel three-loop control system, which supplements control structures known from the literature as model following control (MFC). The major advantage of the proposed system is its high robustness to process parameter variations; it is much higher than that offered by single-loop or two-loop control systems. Features of the new structure are revealed by a theoretical analysis that has been carried out from the viewpoint of requirements for a force/pose controller of a Stäubli RX60 manipulator. This article shows how the proposed control structure responds to such strong process parameter variations and makes a comparison to results yielded by single-loop control structures.     

An extension to output-feedback eigenstructure assignment: Structuring controllers by exploiting unused design freedom

A recent algorithm for output-feedback eigenstructure assignment uses a multi-stage design process to assign a combination of left and right eigenvectors. It is possible, depending on the distribution of design freedom between the stages, that not all of the available degrees of freedom will be used by the assignment process. In this paper, an algorithm is presented which exploits unused design freedom to introduce structure to the resulting gain matrix without affecting the assigned eigenstructure. This process will inevitably have an effect on the overall magnitude of the gains, and consideration is given to this.     

An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems considering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter-varying MPC computes the optimal trajectory in closed-loop by iteratively linearizing the system along the previous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon, while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach.     

Interval-model-based global optimization framework for robust stability and performance of PID controllers

PID controller structure is regarded as a standard in the control-engineering community and is supported by a vast range of automation hardware. Therefore, PID controllers are widely used in industrial practice. However, the problem of tuning the controller parameters has to be tackled by the control engineer and this is often not dealt with in an optimal way, resulting in poor control performance and even compromised safety. The paper proposes a framework, which involves using an interval model for describing the uncertain or variable dynamics of the process. The framework employs a particle swarm optimization algorithm for obtaining the best performing PID controller with regard to several possible criteria, but at the same time taking into account the complementary sensitivity function constraints, which ensure robustness within the bounds of the uncertain parameters’ intervals. Hence, the presented approach enables a simple, computationally tractable and efficient constrained optimization solution for tuning the parameters of the controller, while considering the eventual gain, pole, zero and time-delay uncertainties defined using an interval model of the controlled process. The results provide good control performance while assuring stability within the prescribed uncertainty constraints. Furthermore, the controller performance is adequate only if the relative system perturbations are considered, as proposed in the paper. The proposed approach has been tested on various examples. The results suggest that it is a useful framework for obtaining adequate controller parameters, which ensure robust stability and favorable control performance of the closed-loop, even when considerable process uncertainties are expected.     

Cross-estimator design for coordinated systems: Constraints, covariance, and communications resource assignment

Coordinated systems interact via the exchange of information through communication. As the network of coordinated systems increases in the number of subsystems, natural limits on the available bandwidth of communication need to be imposed. Without this, the coordinated system does not scale properly and the interaction burden becomes unmanageable. Our aim in this paper is to develop estimation techniques for the coordinated systems including the allocation of communication resources. Central ideas involve: interaction via constraints imposed by neighbors, the tightening of these constraints to reflect state uncertainty, and the assignment of communications resources to manage this uncertainty in the estimator design. Linear matrix inequality methods are applied to develop a solution which links control objective, performance, and communication limits.     

An algebraic theory for design of controllers for linear multivariable systems--Part I: Structure matrices and feedforward design

In this two part paper a theory is presented in which several control problems can be solved. The theory is applicable to a general class of linear multivariable systems where the measured output does not have to be equal to the controlled output or the state. The system may be affected by nonmeasurable disturbances. Only controllers which stabilize the system and have proper transfer functions are allowed, i.e., the controllers have to be physically realizable. It is shown that the solutions to control problems of "servo type," e.g., problems of model matching, decoupling, and invertibility, are special cases of the solution to a more general problem. Analogously, the solutions to problems of "regulator type," e.g., disturbance decoupling, output regulation, and pole placement, are also speclal cases of the solution to a more general problem. It is shown that problems of "servo type" and "regulator type" can be solved independently of each other. In Part I generalized polynomials are introduced as a mathematical framework. Structue matrices, which describe how well a system can be controlled, are defined. Finally, problems of "servo type" are solved. Part II mainly deals with problems of "regulator type."     

Model validation for control and controller validation in a prediction error identification framework—Part II: illustrations

In this paper, we illustrate our new results on model validation for control and controller validation in a prediction error identification framework, developed in a companion paper (Gevers et al., Automatica (2003) 39(3) pii: S005-1098(02)00234-0), through two realistic simulation examples, covering widely different control design applications. The first is the control of a flexible mechanical system (the Landau benchmark example) with a tracking objective, the second is the control of a ferrosilicon production process with a disturbance rejection objective.     

From data to diagnosis and control using generalized orthonormal basis filters. Part II: Model predictive and fault tolerant control

Given a state space model together with the state noise and measurement noise characteristics, there are well established procedures to design a Kalman filter based model predictive control (MPC) and fault diagnosis scheme. In practice, however, such disturbance models relating the true root cause of the unmeasured disturbances with the states/outputs are difficult to develop. To alleviate this difficulty, we reformulate the MPC scheme proposed by K.R. Muske and J.B. Rawlings [Model predictive control with linear models, AIChE J. 39 (1993) 262–287] and the fault tolerant control scheme (FTCS) proposed by J. Prakash, S.C. Patwardhan, and S. Narasimhan [A supervisory approach to fault tolerant control of linear multivariable systems, Ind. Eng. Chem. Res. 41 (2002) 2270–2281] starting from the innovations form of state space model identified using generalized orthonormal basis function (GOBF) parameterization. The efficacy of the proposed MPC scheme and the on-line FTCS is demonstrated by conducting simulation studies on the benchmark shell control problem (SCP) and experimental studies on a laboratory scale continuous stirred tank heater (CSTH) system. The analysis of the simulation and experimental results reveals that the MPC scheme formulated using the identified observers produces superior regulatory performance when compared to the regulatory performance of conventional MPC controller even in the presence of significant plant model mismatch. The FTCS reformulated using the innovations form of state space model is able to isolate sensor as well as actuator faults occurring sequentially in time. In particular, the proposed FTCS is able to eliminate offset between the true value of the measured variable and the setpoint in the presence of sensor biases. Thus, the simulation and experimental study clearly demonstrate the advantages of formulating MPC and generalized likelihood ratio (GLR) based fault diagnosis schemes using the innovations form of state space model identified from input output data.     

视觉检测的多分配电梯群控调度算法与仿真设计

为提高电梯群控系统的效率,介绍设计的基于非平稳泊松过程的人流模型和MVC模式的仿真电梯群控系统,同时提出了一种基于视觉检测的多电梯分配算法,该算法可调度多台电梯同时服务一个厅外召唤。在仿真平台上的实验证明,新算法在平均候梯时间和剩余候梯人数指标上明显优于最小最长候梯算法。     

Evolutionary development: a model for the design,implementation, and evaluation of ICT for education programmes

The impact of information and communication technology (ICT) in primary and secondary education is still an open question. Following review of the available literature, we classify the causes of the lack of impact on students' attainment in four dimensions: (1) the design and implementation of ICT in educational settings; (2) the evaluation of its impact; (3) the scaling up of these kinds of innovations; and (4) the cost‐effectiveness of technology‐enhanced learning environments. Based on this evidence, we proposed the evolutionary development model (EDM), which aims to produce a cost‐effective and sustainable ICT for education (ICT4E) programme in three steps: efficacy, effectiveness, and efficiency. In each step, one component of the programme is built and validated in real educational settings. Therefore, the resultant ICT4E programme is ready to be replicated across the school system. We also show how the EDM guided the development of a programme based on mobile computer supported collaborative learning, known as Eduinnova. Finally, we discuss how EDM can serve as an analysis tool for researchers and policy makers.     

Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks

This paper is concerned with the design and implementation of a simple but effective switching control strategy to regulate the dynamics of synthetic gene networks. The testbed circuit is IRMA, a recently developed network in S. Cerevisiae which is proposed as a suitable benchmark problem for control design. The proof-of-concept of an implementation of the control strategy in vivo is presented which is based on the use of microfluidics devices and fluorescence microscopy. Preliminary experimental results are given showing the good matching between the model predictions and the experimental observations.     

Data-driven methods for present and future pandemics: Monitoring,modelling and managing

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.     

MPC of constrained discrete-time linear periodic systems — A framework for asynchronous control: Strong feasibility, stability and optimality via periodic invariance

State-feedback model predictive control (MPC) of discrete-time linear periodic systems with time-dependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. First, periodic controlled and positively invariant sets are characterized, and a method to determine the maximum periodic controlled and positively invariant sets is derived. The proposed periodic controlled invariant sets are then employed in the design of least-restrictive strongly feasible reference-tracking MPC problems. The proposed periodic positively invariant sets are employed in combination with well-known results on optimal unconstrained periodic linear-quadratic regulation (LQR) to yield constrained periodic LQR control laws that are stabilizing and optimal. One motivation for systems with time-dependent dimensions is efficient control law synthesis for discrete-time systems with asynchronous inputs, for which a novel modeling framework resulting in low dimensional models is proposed. The presented methods are applied to a multirate nano-positioning system.