Sašo Blažič

Globally stable direct fuzzy model reference adaptive control

Abstract In the paper a fuzzy adaptive control algorithm is presented. It belongs to the class of direct model reference adaptive techniques based on a fuzzy (Takagi–Sugeno) model of the plant. The plant to be controlled is assumed to be nonlinear and predominantly of the first order. Consequently, the resulting adaptive and control laws are very simple and thus interesting for use in practical applications. The system remains stable in the presence of unmodelled dynamics (disturbances, parasitic high-order dynamics and reconstruction errors are treated explicitly). The global stability of the overall system is proven in the paper, i.e. it is shown that all signals remain bounded while the tracking error and estimated parameters converge to some residual set that depends on the size of disturbance and high-order parasitic dynamics. The proposed algorithm is tested on a simulated three-tank system. Its performance is compared to the performance of a classical MRAC.

Design and Stability Analysis of Fuzzy Model-based Predictive Control—A Case Study

In the paper a fuzzy model based predictive control algorithm is presented. The proposed algorithm is developed in the state space and is given in analytical form, which is an advantage in comparison with optimisation based control schemes. Fuzzy model-based predictive control is potentially interesting in the case of batch reactors, heat-exchangers, furnaces and all the processes with strong nonlinear dynamics and high transport delays. In our case it is implemented to a continuous stirred-tank simulated reactor and compared to optimal PI control. Some stability and design issues of fuzzy model-based predictive control are also given.

Wheeled Mobile Robots Control in a Linear Platoon

Future transportation systems will require a number of drastic measures, mostly to lower traffic jams and air pollution in urban areas. Automatically guided vehicles capable of driving in a platoon fashion will represent an important feature of such systems. Platooning of a group of automated wheeled mobile robots relying on relative sensor information only is addressed in this paper. Each vehicle in the platoon must precisely follow the path of the vehicle in front of it and maintain the desired safety distance to that same vehicle. Vehicles have only distance and azimuth information to the preceding vehicle where no inter-vehicle communication is available. Following vehicles determine their reference positions and orientations based on estimated paths of the vehicles in front of them. Vehicles in the platoon are then controlled to follow the estimated trajectories. Then presented platooning control strategies are experimentally validated by experiments on a group of small-sized mobile robots and on a Pioneer 3AT mobile robot. The results and robustness analysis show the proposed platooning approach applicability.

Quadrocopter Hovering Using Position-estimation Information from Inertial Sensors and a High-delay Video System

The requirement that mobile robots become independent of external sensors, such as GPS, and are able to navigate in an environment by themselves, means that designers have few alternative techniques available. An increasingly popular approach is to use computer vision as a source of information about the surroundings. This paper presents an implementation of computer vision to hold a quadrocopter aircraft in a stable hovering position using a low-cost, consumer-grade, video system. However, such a system is not able to stabilize the aircraft on its own and must rely on a data-fusion algorithm that uses additional measurements from on-board inertial sensors. Special techniques had to be implemented to compensate for the increased delay in the closed-loop system with the computer vision system, i.e., video timestamping to determine the exact delay of the vision system and a slight modification of the Kalman filter to account for this delay. At the end, the validation results of the proposed filtering technique are presented along with the results of an autonomous flight as a proof of the proposed concept.

An approach to predictive control of multivariable time-delayed plant: stability and design issues.

In this paper, a new method of multivariable predictive control is presented. The main advantage of a predictive approach is that multivariable plants with time delays can be easily handled. The proposed control algorithm also introduces a compact and simple design in the case of higher-order and nonminimal phase plants, but it is limited to open-loop stable plants. The algorithm of the proposed multivariable predictive control is developed, designed, and implemented on an air-conditioned system. The stability of the proposed control law is discussed.

Model-Reference Fuzzy Adaptive Control as a Framework for Nonlinear System Control

The paper presents a general methodology of adaptive control based on fuzzy model to deal with unknown plants. The problem of parameter estimation is solved using a direct approach, i.e. the controller parameters are adapted without explicitly estimating plant parameters. Thus, very simple adaptive and control laws are obtained using Lyapunov stability criterion. The generality of the approach is substantiated by 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 control parameters. The proposed control algorithm may be viewed as an extension of classical adaptive control for linear plants, but compared to the latter it provides higher adaptation ability and consequently better performance if the plant is nonlinear. The global stability of the control system is assured and the tracking error converges to the residual set that depends on fuzzification properties. The main advantage of the approach is simplicity that suits control engineers since wide range of industrial processes can be controlled by the proposed method. In the paper, the control of heat exchanger is performed.

A robust fuzzy adaptive law for evolving control systems

In this paper an adaptive law with leakage is presented. This law can be used in the consequent part of Takagi–Sugeno-based control. The approach enables easy implementation in the control systems with evolving antecedent part. This combination results in a high-performance and robust control of nonlinear and slowly varying systems. It is shown in the paper that the proposed adaptive law is a natural way to cope with the parasitic dynamics. The boundedness of estimated parameters, the tracking error and all the signals in the system is guaranteed if the leakage parameter σ′ is large enough. This means that the proposed adaptive law ensures the global stability of the system. A simulation example is given that illustrates the proposed approach.

Simple model of a multi-batch driven pipeline

The problem of modelling and simulating pipelines that are used for transporting different fluids is addressed in the paper. The problem is solved by including fluid density in the model beside pressure and velocity of the medium. First, the system of nonlinear partial differential equations is derived. Then, the obtained model is linearised and transformed into the transfer function form with three inputs and three outputs. Four different forms of model description are presented in the paper. Since transfer functions are transcendent, they cannot be simulated using classical tools. Rational transfer function approximation of the model was found and that simple model was validated on the real industrial pipeline. It was also compared to the model that does not take the changes in fluid density into account. The latter model cannot cope with batch changes whereas the proposed one can.

PDC Control Design for Non-holonomic Wheeled Mobile Robots with Delayed Outputs

This paper presents a new technique for tracking-error model-based Parallel Distributed Compensation (PDC) control for non-holonomic vehicles where the outputs (measurements) of the system are delayed and the delay is constant. Briefly, this technique consists of rewriting the kinematic error model of the mobile robot tracking problem into a TS fuzzy representation and finding a stabilizing controller by solving LMI conditions for the tracking-error model. The state variables are estimated by nonlinear predictor observer where the outputs are delayed by a constant delay. To illustrate the efficiency of the proposed approach a comparison between the TS fuzzy observer and the nonlinear predictor observer is shown. For this study the reference trajectory is built by taking into account the acceleration limits of the mobile robot. All experiments are implemented on simulation and the real-time platform.

Image-Based Attitude Control of a Remote Sensing Satellite

This paper deals with the image-based control of a satellite for remote sensing. Approach is demonstrated by simulation where the position of the satellite is obtained with the Simplified General Perturbations Version 3 model and its orientation by simulating its dynamic and kinematic models. For a known position and orientation of the satellite the images are obtained using the satellite’s onboard camera, simulated by the Google Earth application. The orientation of the satellite is governed by reaction wheels, which produce the required moments to the satellite. The image-based control law using SIFT image features is applied to achieve an automatic reference-point observation on the Earth’s surface. Main contributions of the paper are the following: use of the same sensor for Earth observation and attitude control, simplicity of the approach, no need for explicit calibration of camera parameters and good tracking accuracy. Demonstrated simulation results and performance analysis confirm the approach applicability.