Observer-based offset-free internal model control

A linear feedback control structure is proposed that allows internal model control design principles to be applied to unstable and marginally stable plants. The control structure comprises an observer using an augmented plant model, state estimate feedback and disturbance estimate feedback. Conditions are given for both nominal internal stability and offset-free action even in the case of plant-model mismatch. The Youla parameterization is recovered as a limiting case with reduced order observers. The simple design methodology is illustrated for a marginally stable plant with delay

Autotuning for delay systems using meromorphic functions

The paper presents an autotuning method for time delay systems. The novelty in principles is a new combination of biased-relay feedback identification and an algebraic control design method for timedelay systems. The estimation of the controlled process is based on an asymmetrical limit cycle data experiment. Then, a stable transfer function with a dead-time term is identified. The controller is designed through solutions of Diophantine equations in the ring of stable and proper retarded quasipolynomial meromorphic functions. Controller parameters are tuned through a pole-placement problem as a desired multiple root of the characteristic closed loop equation. First and second order identification gives Smith-like feedback controllers with the realistic PI and PID structure. The design principle also offers a scalar tuning parameter m 0 > 0 which can be adjusted by a suitable principle or an optimization method. The developed approach is illustrated by examples in the Matlab + Simulink environment

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs for both linear-time-invariant (LTI) single-input-single-output (SISO) systems and certain classes of nonlinear systems. In this dissertation, a data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance. Necessary and sufficient conditions are derived for obtaining H∞ performance while guaranteeing the closed-loop stability of a system. A convex optimization algorithm is implemented to obtain the controller parameters which ensure system robustness; the controller is robust with respect to the frequency-dependent uncertainties of the frequency response function. For a certain class of nonlinearities, the proposed method can be used to obtain a best-linear-approximation with an associated frequency dependent uncertainty to guarantee the stability and performance for the underlying linear system that is subject to nonlinear distortions. The concepts behind these design methods are then used to devise necessary and sufficient conditions for ensuring the closed-loop stability of systems with sector-bounded nonlinearities. The conditions are simple convex feasibility constraints which can be used to stabilize systems with multi-model uncertainty. Additionally, a method is proposed for obtaining H∞ performance for an approximate model (i.e., describing function) of a sector-bounded nonlinearity. This work also proposes several data-driven methods for designing robust fixed-structure controllers with H∞ performance. One method considers the solution to a non-convex problem, while another method convexifies the problem and implements an iterative algorithm to obtain the local solution (which can also consider H2 performance). The effectiveness of the proposed method(s) is illustrated by considering several case studies that require robust controllers for achieving the desired performance. The main applicative work in this dissertation is with respect to a power converter control system at the European Organization for Nuclear Research (CERN) (which is used to control the current in a magnet to produce the desired field in controlling particle trajectories in accelerators). The proposed design methods are implemented in order to satisfy the challenging performance specifications set by the application while guaranteeing the system stability and robustness using data-driven design strategies

Operability analysis of chemical processes based on passivity

Process operability is fundamentally determined by the design of the process. An easy to operate process can achieve a good control performance even by using a very simple controller. On the other hand, regardless how good the control system is, when applied to a poorly designed process, it may not deliver the desired control performance. Operability analysis determines whether a process can be controlled effectively using a feedback control system. Such analysis is very useful in revealing potential operability problems in the early stages of process design. A vast amount of operability analysis methods have been developed based on open-loop models. These methods can be used without the need to design the control system for closed-loop simulations, hence remove the arbitrariness and lengthiness of the simulation based approach commonly applied in industry. However, most of the open-loop operability analysis methods can only be used for stable linear processes with specific control structures and pairings. Only a few methods are available for unstable linear processes and nonlinear processes. In this thesis, we develop a systematic approach to operability analysis of linear and nonlinear processes based on the concept of passive systems. Passive systems represent a class of minimum phase systems with relative degree no larger than one. This implies strictly passive systems are very easy to control. They can be controlled by any passive controller with any infinitely large positive gain. This also implies that the degree of passivity of a system can be used to indicate its operability. Moreover, the passivity theory is applicable not only for linear processes but also for nonlinear processes. This makes the passivity based method an excellent candidate for operability analysis of chemical processes. A chemical process in general consists of more than one unit operation. To assess the operability of multi-unit linear processes under various control structures and pairings, we develop a simple dynamic operability analysis method by extending the passivity based DIC and BDIC conditions to higher frequencies. Furthermore, since the operability of unstable processes is often a bigger concern in control practice, we also develop a dynamic operability analysis method that can be used for both stable and unstable linear processes based on coprime factorization and feedforward passivation. Operability analysis for nonlinear processes is often perceived as a very complex and computational demanding exercise by practitioners. To bridge the gap between existing passivity based operability analysis methods and their implementations, we develop a numerical framework for assessing the steady-state and dynamic operability of a nonlinear process solely based on the input-output data obtained from a process simulator. As such, it can be implemented conveniently as one extra step after the flowsheet simulation. Unlike linear processes, the passivity of nonlinear processes is usually defined with respect to one particular operating point of interest. To address the operability at various operating points, we also develop a dynamic operability analysis method for nonlinear processes based on incremental passivity. Throughout this thesis, we demonstrate how the passivity based analysis can be used across several process characteristics and requirements of practical interest

Decentralized Robust Capacity Control of Job Shop Systems with Reconfigurable Machine Tools

Manufacturing companies are confronted with various challenges from the perspective of customers individual requirements concerning variations of types of products, quantities and delivery dates. This renders the manufacturing process to be more dynamic and complex, which may result in bottlenecks and unbalanced capacity distributions. To cope with these problems, capacity adjustment is an effective approach to balance capacity and load for short or medium term fluctuations on the operational layer. Particularly, new technologies and algorithms need to be developed for the implementation of capacity adjustment. Reconfigurable machine tools (RMTs) and operator-based robust right coprime factorization (RRCF) provide an opportunity for a new capacity control strategy. Therefore, the main purpose of the research is to develop an effective machinery-oriented capacity control strategy by incorporating RMTs and RRCF for a job shop system to deal with volatile customer demands

Multivariable Adaptive Control Design Under Internal Model Control Structure.

A new adaptive multivariate control scheme has been devised. The method combines the best characteristics of conventional adaptive systems and internal model control (IMC) structure. The control scheme builds by itself the required models and avoids the ambiguities in the definition of performance specifications. The problem of plant inversion associated with the IMC structure has been solved. The method introduced in this work is based on the properties of the Smith-McMillan form. However, the method does not require the explicit determination of the form. Furthermore, the computation of a stable plant inverse requires only matrix inversion and scalar polynomial factorization. The resulting algorithm is suitable for on-line operation. The control schemed is implemented through the following stages: (1) Identification. The parameters of a multivariable ARX model are estimated using a recursive least square algorithm with variable forgetting factor. The input and output orders can be used as additional degrees of freedom. The algorithm developed shows good numerical characteristics with fast convergence even for a large number of parameters. (2) Computation of the manipulated variables. The model is used to determine a controller following the IMC approach. The resulting equations are solved to compute the required manipulated variables. The algorithm for system inversion allows computations to be executed on-line. (3) Filtering. The usual filters of the IMC approach are also used in the adaptive scheme. The objective is to reduce the sensitivity of the controller. Only non-adaptive non-interactive filters have been considered. The results with first order low pass filters are satisfactory. The bandwidth of the filter is used as an additional tuning parameter. The adaptive control strategy has been extensively tested using computer simulation. The tests include extensions to non-linear plants. Comparisons with non-adaptive IMC control show the advantage of the new scheme developed in this work