Drago Matko

Time optimal path planning considering acceleration limits

A robot path planning technique is proposed in the paper. It was developed for robots with differential drive, but with minor modifications it could be used for all types of nonholonomic robots. The path was planned in the way to minimise the time of reaching the end point in desired direction and with desired velocity, starting from the initial state described by the start point, initial direction and initial velocity. The limitation was the grip of the tires that results in the acceleration limits. The path is presented as a spline curve and was optimised by placing the control points through which the curve should pass.

Application of Model Reference Adaptive Control to Industrial Robot Impedance Control

The paper deals with the application of model reference adaptive control to robot impedance control, which is actually a technique of steering the end-effector on a prescribed path and satisfying a prescribed dynamic relationship between the force and the end-effector position. Due to unknown parameters of the environment (stiffness, exact position), a model reference algorithm is proposed which differs from classical algorithms in its method of excitation. The results of the proposed procedure are illustrated by implementation on the ASEA IRb 6 industrial robot.

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.

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.

Fuzzy predictive control of highly nonlinear pH process

Abstract This paper proposes a new approach to predictive control of highly nonlinear processes based on a fuzzy model of the Takagi-Sugeno form. Standard Model Based Predictive Control (MBPC) methods use linear process models and are therefore unable to deal with strong process nonlinearities. But, the advantage of linear MBPC is in implementation due to fast optimization algorithms and guaranteed convergence within each time sample. In our approach, step responses for different operating points are extracted on-line from the nonlinear fuzzy model and a modified linear DMC algorithm is used. In this way, all the advantages of both fuzzy modeling of nonlinear processes and DMC control are accomplished. For performance evaluation of this simple but efficient approach, a nonlinear pH-process is used.

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.

Wiener model based nonlinear predictive control

This paper addresses the problem of discrete-time nonlinear predictive control of W iener systems. Wiener-model-based nonlinear predictive control combines the advantages of linear-model-based predictive control and gain scheduling while retaining a moderate level of computational complexity. A clear relation is shown between an iteration in the optimization of the nonlinear control problem and the control problem of the underlying linear-model-based method. This relation has a simple form of gain scheduling, thus the properties of the nonlinear control system can be analysed from the comprehensible linear control aspect. Several disturbance rejection techniques are proposed and compared. The method was tested on a simulated model of a pH neutralization process. The performance was excellent also in the case of a considerable plant-tomodel mismatch. The method can be applied as a first next step in cases where the performance of linear control is unsatisfactory owing to process nonlinearity.

Fuzzy Predictive Functional Control in the State Space Domain

In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.

A control strategy for platoons of differential drive wheeled mobile robot

The strategy for the control of vehicle platooning is proposed and tested on different mobile robot platforms. The decentralized platooning is considered, i.e. a virtual train of vehicles where each vehicle is autonomous and decides on its motion based on its own perceptions. The following vehicle only has information about its distance and azimuth to the leading vehicle. Its position is determined using odometry. The reference position and the orientation of the following vehicle are determined by the estimated path of the leading vehicle in a parametric polynomial form. The parameters of the polynomials are determined using the least-squares method. This parametric reference path is also used to determine the feed-forward part and to suppress tracking errors by a feed-back part of the applied globally stable nonlinear control law. The results of the experiment and simulations demonstrate the applicability of the proposed algorithm for vehicle platoons.

Quadrocopter control using an on-board video system with off-board processing

In recent years, Unmanned Aerial Vehicles (UAVs) have gained increasing popularity. These vehicles are employed in many applications, from military operations to civilian tasks. One of the main fields of UAV research is the vehicle positioning problem. Fully autonomous vehicles are required to be as self-sustained as possible in terms of external sensors. To achieve this in situations where the global positioning system (GPS) does not function, computer vision can be used. This paper presents an implementation of computer vision to hold a quadrotor aircraft in a stable hovering position using a low-cost, consumer-grade, video system. The successful implementation of this system required the development of a data-fusion algorithm that uses both inertial sensors and visual system measurements for the purpose of positioning. The system design is unique in its ability to successfully handle missing and considerably delayed video system data. Finally, a control algorithm was implemented and the whole system was tested experimentally. The results suggest the successful continuation of research in this field.