Poboljšana estimacija položaja za navigaciju vozila koristeći poravnavanje sustava i unaprijedno izglađivanje

In this paper we present the application of a modified Extended Kalman filter on a test device. This device is intended to be both rugged and simple to use, providing an accurate position and velocity output for land vehicles. As there are already many applications available for this purpose, our test device is unique in a way that it can be mounted in an arbitrary position on any metal surface on a vehicle, while it automatically discovers its orientation and aligns itself during the first stage of the test period. It comes as an extremely robust and low-cost solution. Furthermore, the pre-alignment outputs are corrected using a reverse output correction during the test period, immediately providing accurate outputs. The alignment algorithm greatly (by factor of 20 or more) reduces the initialization time. In addition, a novel smoothing algorithm with forward computation is described. The developed algorithm is tested with real-world experiments and proved to have a similar accuracy as the reference system, although using much cheaper and less-reliable sensor equipment.U ovome radu opisana je implementacija modificiranog proširenog kalmanovog filtra na testnom uređaju. Uređaj je robustan i jednostavan za upotrebu te omogućava dobivanje točne pozicije i brzine kao izlaznih parametara zemljanih vozila. S obzirom da postoji velik broj postojećih aplikacija, naš testni uređaj je jedinstven u smislu da može biti postavljen u proizvoljnu poziciju na metalnu površinu na vozilu. Uređaj automatski određuje svoju orijentaciju te se poravnava tijekom prvog dijela testnog perioda. Ujedno je vrlo robustan i niske cijene. Izlaz koji se dobije prije poravnavanja ispravljen je koristeći obrnutu korekciju izlaza tijekom testne faze čime se dobiju točni izlazi. Algoritam poravnavanja znatno (za faktor 20 i više) reducira vrijeme potrebno za inicijalizaciju. Opisan je novi algoritam izglađivanja s unaprijednim proračunom. Razvijeni algoritam testiran je na stvarnim podacima te je pokazano da ima sličnu preciznost kao referentni sustav, unatoč korištenju jeftinije i manje pouzdane opreme

Effective parametrization of low order Bézier motion primitives for continuous-curvature path-planning applications

We propose a new parametrization of motion primitives based on Bézier curves that suits perfectly path-planning applications (and environment exploration) of wheeled mobile robots. The individual motion primitives can simply be calculated taking into account the requirements of path planning and the constraints of a vehicle, given in the form of the starting and ending orientations, velocities, turning rates, and curvatures. The proposed parametrization provides a natural geometric interpretation of the curve. The solution of the problem does not require optimization and is obtained by solving a system of simple polynomial equations. The resulting planar path composed of the primitives is guaranteed to be C2 continuous (the curvature is therefore continuous). The proposed primitives feature low order Bézier (third order polynomial) curves. This not only provides the final path with minimal required turns or unwanted oscillations that typically appear when using higher-order polynomial primitives due to Runge’s phenomenon but also makes the approach extremely computationally efficient. When used in path planning optimizers, the proposed primitives enable better convergence and conditionality of the optimization problem due to a low number of required parameters and a low order of the polynomials. The main contribution of the paper therefore lies in the analytic solution for the third-order Bézier motion primitive under given boundary conditions that guarantee continuous curvature of the composed spline path. The proposed approach is illustrated on some typical scenarios of path planning for wheeled mobile robots

Comparison of approaches for identification of all-data cloud-based evolving systems

In this paper we deal with identification of nonlinear systems which are modelled by fuzzy rule-based models that do not assume fixed partitioning of the space of antecedent variables. We first present an alternative way of describing local density in the cloud-based evolving systems. The Mahalanobis distance among the data samples is used which leads to the density that is more suitable when the data are scattered around the input-output surface. All the algorithms for the identification of the cloud parameters are given in a recursive form which is necessary for the implementation of an evolving system. It is also shown that a simple linearised model can be obtained without identification of the consequent parameters. All the proposed algorithms are illustrated on a simple simulation model of a static system

Tracking control for wheeled mobile robot based on delayed sensor measurements

This paper proposes a novel Takagi-Sugeno fuzzy predictor observer to tackle the problem of the constant and known delay in the measurements. The proposed observer is developed for a trajectory-tracking problem of a wheeled mobile robot where a parallel-distributed compensation control is used to control the robot. The L2-stability of the proposed observer is also proven in the paper. Both, the control and the observer gains are obtained by solving the proposed system of linear matrix inequalities. To illustrate the efficiency of the proposed approach, an experimental comparison with another predictor observer was done

Stabilization of the cart–inverted-pendulum system using state-feedback pole-independent MPC controllers

In this paper, a pole-independent, single-input, multi-output explicit linear MPC controller is proposed to stabilize the fourth-order cart–inverted-pendulum system around the desired equilibrium points. To circumvent an obvious stability problem, a generalized prediction model is proposed that yields an MPC controller with four tuning parameters. The first two parameters, namely the horizon time and the relative cart–pendulum weight factor, are automatically adjusted to ensure a priori prescribed system gain margin and fast pendulum response while the remaining two parameters, namely the pendulum and cart velocity weight factors, are maintained as free tuning parameters. The comparison of the proposed method with some optimal control methods in the absence of disturbance input shows an obvious advantage in the average peak efficiency in favor of the proposed SIMO MPC controller at the price of slightly reduced speed efficiency. Additionally, none of the compared controllers can achieve a system gain margin greater than 1.63, while the proposed one can go beyond that limit at the price of additional degradation in the speed efficiency

Tracking Control for Wheeled Mobile Robot Based on Delayed Sensor Measurements

This paper proposes a novel Takagi-Sugeno fuzzy predictor observer to tackle the problem of the constant and known delay in the measurements. The proposed observer is developed for a trajectory-tracking problem of a wheeled mobile robot where a parallel-distributed compensation control is used to control the robot. The L2-stability of the proposed observer is also proven in the paper. Both, the control and the observer gains are obtained by solving the proposed system of linear matrix inequalities. To illustrate the efficiency of the proposed approach, an experimental comparison with another predictor observer was done

MPC Control and LQ Optimal Control of A Two-Link Robot Arm: A Comparative Study

This study examined the control of a planar two-link robot arm. The control approach design was based on the dynamic model of the robot. The mathematical model of the system was nonlinear, and thus a feedback linearization control was first proposed to obtain a linear system for which a model predictive control (MPC) was developed. The MPC control parameters were obtained analytically by minimizing a cost function. In addition, a simulation study was done comparing the proposed MPC control approach, the linear quadratic (LQ) control based on the same feedback linearization, and a control approach proposed in the literature for the same problem. The results showed the efficiency of the proposed method

MPC control and LQ optimal control of a two-link robot arm

This study examined the control of a planar two-link robot arm. The control approach design was based on the dynamic model of the robot. The mathematical model of the system was nonlinear, and thus a feedback linearization control was first proposed to obtain a linear system for which a model predictive control (MPC) was developed. The MPC control parameters were obtained analytically by minimizing a cost function. In addition, a simulation study was done comparing the proposed MPC control approach, the linear quadratic (LQ) control based on the same feedback linearization, and a control approach proposed in the literature for the same problem. The results showed the efficiency of the proposed method

Stabilization of the cart-inverted-pendulum system using trivial state-feedback to output-feedback control conversion

A new linear observer-free output-feedback controller with five adjustable parameters is proposed to stabilize the cart-inverted-pendulum system (CIP) at the unstable equilibrium point. The controller architecture is deduced from a trivial conversion of the linear state-feedback controller that is obtained using a two-step method. First, based on a set of cart change variables, a slightly modified state-feedback controller is developed. Then, the output-feedback controller is obtained through the judicious combination of the cart step reference input internal model and a convenient open-loop state estimator with the above modified state-feedback controller. The local stability of the output-based control system is conducted using the signature formulas method to get simplified conditions. A partial single parameter tuning method and optimal global single parameter tuning method are proposed for adjusting the controller gains to maximize a new efficiency-based objective function. Numerical simulations are first conducted to reveal the simplicity of output-feedback controller design using the partial tuning method, where the state-feedback gains are assumed to be known. Then, an optimal output-feedback controller is designed using the global tuning method. The proposed output-feedback controller is equivalent in terms of performance efficiency to the best five-parameter output-feedback two PID controller