WAVES: towards real time laboratory experiments in cyberspace

Hibridación de sistemas borrosos para el modelado y control

This paper discusses an experiment-based approach to teaching an advanced control engineering syllabus involving controlled plant analysis and modeling, control structures and algorithms, real-time laboratory experiments, and their assessment. These experiments are structured around the representative case of the longitudinal slip control of an antilock braking system (ABS). The basic and advanced control strategies that are experimentally studied include the proportional-integral (PI) control, gain scheduling, iterative feedback tuning (IFT) and fuzzy control. The final grades obtained by representative groups of students demonstrate the efficiency of a described hands-on laboratory experimental study of a complex antilock braking system as encountered in practice, which allowed students to gain a better understanding of the theoretical aspects learned during the lectures .

Frequency response function (FRF) estimation from measured data is an essential step in the design, control, and analysis of complex dynamical systems, including thermal and motion systems. Especially for systems that require long measurement time, missing samples in the data record, e.g., due to measurement interruptions, often occur. The aim of this article is to achieve accurate identification of nonparametric FRF models of periodically excited systems from noisy output measurements with missing samples. An identification framework is established that exploits a wavelet-based transform to separate the effect of the missing samples in the time domain from the system characteristics in tre frequency domain. The framework encompasses both a time-invariant and a time-varying wavelet-based estimator, which provides different mechanisms to address the missing samples. Experimental results from a thermodynamical system confirm that the estimators enable accurate identification.

A First Laboratory Course in Analysis and Design of Dynamic Systems

Chapter 3 - Neural Networks: A Methodology for Modeling and Control Design of Dynamical Systems

Dynamic systems such as robots, autonomous vehicles, and process plants require careful design and control to achieve optimal performance. This paper presents an integrated framework for the simultaneous optimization of system design parameters and control policies. The underlying mathematical formulation utilizes optimization techniques to find the best system configuration and control strategy according to specified objectives. These objectives may include metrics such as tracking error, energy consumption, safety margins, and cost. Both model-based and data-driven techniques are explored for learning the system dynamics and synthesizing optimal controllers. The optimization algorithms leveraged include gradient-based methods, evolutionary algorithms, and reinforcement learning. The benefits of the joint optimization approach are demonstrated through case studies on representative dynamic systems. Results show that the integrated design and control framework outperforms sequential optimization, leading to improved efficiency, responsiveness, and robustness. This underscores the importance of co-optimizing design and control parameters, especially for complex, uncertain systems. The proposed methods provide an effective tool for next-generation automated design of smart, adaptable systems.

Simultaneous design and control under uncertainty: A back-off approach using power series expansions

In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plant’s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plant’s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation results showed that RBFN is able to capture the unknown dynamics as well as simultaneously able to adaptively control the plant. It is also found to compensate the effects of parameter variations and disturbances. The comparative analysis is also done with MLFFNN in each simulation example, and it is found that performance of RBFN is better than that of MLFFNN.

Neural networks for modelling and control of dynamic systems, M. Nørgaard, O. Ravn, N. K. Poulsen and L. K. Hansen, Springer, London, 2000, xiv+246pp.

Fractional-order Modeling and Control of Dynamic Systems

Is it possible to predict the future? How accurate is the prediction for the future? These questions are fascinating and intriguing ones in particular for young generations who look at their future with curiosity. For a long time, many have tried to quantitatively predict future behavior of systems more accurately with techniques such as time series analysis and derived dynamical models based on observed data. The paper proposes a lecture structure in which elements of chaos, which greatly impacts the predictive capabilities of dynamical models, are introduced through two classical examples of nonlinear dynamical systems, namely Lorenz attractor and Lotka-Volterra equations. In a possible lecture, these two structures are introduced in a basic and intuitive way, followed by equilibria analyses and Lyapunov control approaches. The paper intends to give a possible structure of an interdisciplinary lecture in chaotic systems, for all students in general and non-engineering students in particular, to kindle students’ interest in challenging ideas and models. By presenting an intuitive learning-based approach and the results of the implementation, the paper contributes to the discourse on interdisciplinary education. The lecture is a part of a course within a Complementary Study at Leuphana Unversity of Lüneburg. The material which inspired the proposed lecture structure is taken from the scripts of the Master Complementary Course titled Modelling and Control of Dynamical Systems using Linear and Nonlinear Differential Equations held at Leuphana University of Lüneburg.

Neural networks for modelling and control of dynamic systems: M. Nørgaard, O. Ravn, N. K. Poulsen and L. K. Hansen. Springer-Verlag, London Berlin Heidelberg, 2000, pp. xiv+246

Neural Networks for Modelling and Control of Dynamic Systems

Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GA's). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of an FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.

This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive Control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal of the model (from the previous sampling instant) are used as the inputs of the network. In order to obtain a computationally simple MPC algorithm, the nonlinear Jordan neural model is repeatedly linearised on-line around an operating point, which leads to a quadratic optimisation problem. Effectiveness of the described MPC algorithm is compared with that of the truly nonlinear MPC scheme with on-line nonlinear optimisation performed at each sampling instant.