Saso Blazic

Direct fuzzy model-reference adaptive control

Intelligent systems may be viewed as a framework for solving the problems of nonlinear system control. The intelligence of the system in the nonlinear or changing environment is used to recognize in which environment the system currently resides and to service it appropriately. This paper presents a general methodology of adaptive control based on multiple models in fuzzy form to deal with plants with unknown parameters which depend on known plant variables. We introduce a novel model‐reference fuzzy adaptive control system which is based on the fuzzy basis function expansion. The generality of the proposed algorithm is substantiated by the Stone‐Weierstrass theorem which indicates that any continuous function can be approximated by fuzzy basis function expansion. In the sense of adaptive control this implies the adaptive law with fuzzified adaptive parameters which are obtained using Lyapunov stability criterion. The combination of adaptive control theory based on models obtained by fuzzy basis function expansion results in fuzzy direct model‐reference adaptive control which provides higher adaptation ability than basic adaptive‐control systems. The proposed control algorithm is the extension of direct model‐reference fuzzy adaptive‐control to nonlinear plants. The direct fuzzy adaptive controller directly adjusts the parameter of the fuzzy controller to achieve approximate asymptotic tracking of the model‐reference input. The main advantage of the proposed approach is simplicity together with high performance, and it has been shown that the closed‐loop system using the direct fuzzy adaptive controller is globally stable and the tracking error converges to the residual set which depends on fuzzification properties. The proposed approach can be implemented on a wide range of industrial processes. In the paper the foundation of the proposed algorithm are given and some simulation examples are shown and discussed.

Mobile Robot Control on a Reference Path

In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well

Interval fuzzy modeling applied to Wiener models with uncertainties

This correspondence addresses the problem of interval fuzzy model identification and its use in the case of the robust Wiener model. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion which minimizes the maximum estimation error between the data and the proposed fuzzy model output is used. The min-max optimization problem can then be seen as a linear programming problem that is solved to estimate the parameters of the fuzzy model in each fuzzy domain. This results in lower and upper fuzzy models that define the confidence interval of the observed data. The model is called the interval fuzzy model and is used to approximate the static nonlinearity in the case of the Wiener model with uncertainties. The resulting model has the potential to be used in the areas of robust control and fault detection.

A practical implementation of self-evolving cloud-based control of a pilot plant

This paper presents the implementation of a first order self-evolving cloud-based controller for the liquid level of a two-tank pilot plant. The controller is based on the AnYa type fuzzy rule-based system (FRB), which has a parameter-free antecedent part, and can learn autonomously on-line with each new input data collected and output generated, with no prior knowledge of the system or off-line training. Two types of controllers are considered: a PD-type controller, with simulated and real results; and a MRC-type controller, with simulated results. Regarding the practical implementation, a real continuous process didactic plant was used as a representation of a real industrial environment through the OLE for Process Control (OPC) communication protocol. It has been demonstrated the possibility of building autonomously and in an unsupervised manner a controller capable of developing and adapting itself in a real-time industrial automation application.

On Periodic Control Laws for Mobile Robots

This paper deals with the control of differentially driven wheeled mobile robots. Two families of wheeled mobile robots are considered: those that are capable of forward motion only and those that can perform forward and backward motion. A unified framework for the control law analysis and design of both robot types is proposed. The control laws are developed within a Lyapunov stability analysis framework. Periodic Lyapunov functions are proposed, and the constructive procedure leads to periodic control laws. These laws are very natural for wheeled mobile robots since they are inspired by the periodic nature of a robots orientation. The simple form of the control laws enables their easy implementation in practical applications. Global convergence is proven based on the usual requirements for reference velocities. Some important properties of these systems are also treated, such as continuity and the presence of unstable equilibria. Some guidelines for how to choose a suitable control law and its parameters are also given. An extensive simulation study was performed, and the results of the proposed control laws are compared with some control laws from the literature. The algorithms were also validated using the Mirosot-type robot soccer robot and a vision-based system.

Cloud-based identification of an evolving system with supervisory mechanisms

The paper deals with identification of a cloud based evolving system. The antecedent part of a fuzzy rule-based system is defined by clouds and density distribution as proposed by Angelov and Yager [1], [2]. But in the current paper Mahalanobis distance is used rather than the Euclidean one when calculating the density. The idea behind is that the shape of the clouds should be reflected in the density calculation. Covariance matrix of the elements in the clouds is needed for this purpose and it is obtained by a recursive algorithm. An important part of the paper is devoted to supervisory mechanisms that enable higher robustness of the identification. The use of buffers of data are promoted that enable balanced use of batch and recursive identification. This part is still work in progress. The proposed algorithms are illustrated on a simulated pH-neutralisation process.

Predictive Functional Control Based on Fuzzy Model: Design and Stability Study

In the paper the design methodology and stability analysis of parallel distributed fuzzy model based predictive control is presented. The idea is to design a control law for each rule of the fuzzy model and blend them together. The proposed control algorithm is developed in state space domain and is given in analytical form. The analytical form brings advantages in comparison with optimization based control schemes especially in the sence of realization in real-time. The stability analysis and design problems can be viewed as a linear matrix inequalities problem. This problem is solved by convex programming involving LMIs. In the paper a sufficient stability condition for parallel distributed fuzzy model-based predictive control is given. The problem is illustrated by an example on magnetic suspension system.In the paper the design methodology and stability analysis of parallel distributed fuzzy model based predictive control is presented. The idea is to design a control law for each rule of the fuzzy model and blend them together. The proposed control algorithm is developed in state space domain and is given in analytical form. The analytical form brings advantages in comparison with optimization based control schemes especially in the sence of realization in real-time. The stability analysis and design problems can be viewed as a linear matrix inequalities problem. This problem is solved by convex programming involving LMIs. In the paper a sufficient stability condition for parallel distributed fuzzy model-based predictive control is given. The problem is illustrated by an example on magnetic suspension system.

Robust evolving cloud-based controller for a hydraulic plant

In this paper a novel online self-evolving cloud-based controller (RECCo) is introduced. This type of controller has a parameter-free antecedent (IF) part. Two types of consequents are proposed - a locally valid PID-type controller and a locally valid MRC-type one. Corresponding adaptive laws are proposed to tune the parameters in consequent part autonomously. This RECCo controller learns autonomously from its own actions while performing the control of the plant. It does not use any off-line pre-training nor the explicit model (e.g. in a form of differential equations) of the plant. It has been demonstrated that a fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hyper-surface acting as a data space) it is possible to generate and self-tune/learn a non-linear controller structure and evolve it in on-line mode. The proposed algorithm is tested on a simulated hydraulic plant. The example is provided aiming mainly to prove the proposed concept.

Robust evolving cloud-based PID control adjusted by gradient learning method

In this paper an improved robust evolving cloud-based controller (RECCo) for a class of nonlinear processes is introduced. The controller is based on parameter-free premise (IF) part. The consequence in this case is given in the form of PID-type controller. The three adjustable parameters of PID controller are updated on-line with a stable adaptation mechanism based on Lyapunov approach such that the output of the process tracks the desired model-reference trajectory. The proposed algorithm has also ability to add new rules or new clouds when this is necessary to improve the whole behaviour of the controlled process. This means that RECCo controller evolves the control structure and adjusts at the same time the parameters of the controller in an on-line manner, while performing the control of the plant. This approach is an example of almost parameter-free approach, because it does not use any off-line pre-training nor the explicit model of the plant and requires almost no parameter tuning. The proposed algorithm is tested on an artificial nonlinear first-order process and on a simulated hydraulic plant.

Virtual race as an examination test: models, solutions, experiences

In this paper, an idea is presented on how to motivate students to learn an exacting subject such as computer-aided control systems design (CACSD). The idea is that the exam is a virtual race or competition meaning that the students are graded according to the success achieved in the competition. The aim of the competition is set by the professor, and the students have to design a controller to achieve the best possible result they can. Four competition subjects, those used in the last four years of CACSD education at the Faculty of Electrical Engineering, University of Ljubljana, are reviewed with emphasis on the fourth one-the surf regatta. The mathematical model of the surf is then presented. In all four cases, the criterion for the competition was the shortest time. The paper concludes with the description of solutions applied by students and the faculty experiences with the course.