This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor–trailer system track both linear and curvilinear target trajectories with low tracking error.

Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles

This paper presents a reduced-order extended state observer (ESO) based sliding mode control scheme for friction compensation of a three-wheeled omnidirectional mobile robot. Compared with previous works, the proposed control approach is attractive from an implementation point of view. It does not require any explicit friction model, with quite low computation cost. First, a dynamic model with unknown friction forces is given. Then, the controller is designed, consisting of two parts. One part of the control effort is to compensate the friction effects, which are estimated by a reduced-order ESO without using any explicit friction model. The inverse of inertia matrix is also avoided in the proposed reduced-order ESO. The other part of the control effort is designed based on a second-order sliding mode technique known as super-twisting algorithm, in presence of parameter uncertainties. In addition, stability analysis of the designed control system is presented. Extensive experiments are conducted to verify the effectiveness and robustness of the proposed control design in compensating different friction effects.

Extended State Observer-Based Sliding Mode Control of an Omnidirectional Mobile Robot With Friction Compensation

This paper deals with the design of a robust hierarchical multiloop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system and the combination of a model predictive control (MPC) module with an integral sliding mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodeled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfiling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the MPC optimization problem relying on a set of linearized decoupled single-input-single-output (SISO) systems that are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data.

MPC for Robot Manipulators With Integral Sliding Modes Generation

A hierarchically improved fuzzy dynamical sliding-model control (HIFDSMC) is presented to address the autonomous ground vehicle (AGV) path tracking problem. The proposed controller has two portions: one is the virtual desired input (VDI), and the second is the path tracking control (PTC). In addition to the equivalent control in VDI and PTC, an improved fuzzy dynamical sliding-mode control (IFDSMC) is designed to deal with the system uncertainties, e.g., different payloads. Contributions of this paper include the following four parts: 1) Based on the nominal system response, the fuzzy rules and scaling factors of the IFDSMCs in the VDI and PTC are easily chosen. In contrast, a conventional fuzzy logic control approach requires more trial-and-error tuning to obtain a satisfactory performance. 2) The proposed HIFDSMC possesses the tuning mechanism (the coefficients of two sliding surfaces, the scaling factors in indirect and direct modes, and the fine tuning in fuzzy table) such that the uncertainties are tackled without a larger computational burden. 3) The stability of the closed-loop system is verified by the Lyapunov stability with hierarchical concept. 4) Different payloads not at the mass center of the AGV (e.g., greater than 25% in the total weight of the AGV) are tackled by the IFDSMCs to obtain a satisfactory performance. Finally, the application to the assembly line of the AGV with different payloads to track the circular and piecewise straight-line paths by the proposed HIFDSMC is compared with the hierarchical fuzzy decentralized PTC.

Path Tracking of an Autonomous Ground Vehicle With Different Payloads by Hierarchical Improved Fuzzy Dynamic Sliding-Mode Control

https://publikationen.bibliothek.kit.edu/1000118014/66129147

Reviewing energy system modelling of decentralized energy autonomy

This paper presents and discusses the implementation results of a model-predictive control (MPC) scheme with friction compensation applied to trajectory following of an omnidirectional three-wheeled robot. A cascade structure is used with an inverse kinematics block to generate the velocity references given to the predictive controller. Part of the control effort is used to compensate for the effects of static friction, allowing the use of efficient algorithms for linear MPC with constraints. Experimental results show that the proposed strategy is efficient in compensating for frictional effects as well as for tracking predefined trajectories.

Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot

Undeniably the basic needs of a man are numerous. A traditional list includes food, shelter and clothing. However, for a contemporary man, the lack of access to electricity is strongly connected to the social exclusion. In Brazil, isolate villages is still a reality because the costs to expand the national grid can be unattractive. An alternative solution is to design an off-grid power system, normally using a diesel generator. This paper investigates the costs of a micro-grid developed for an isolate community located in amazon region. Their costs can be minimized if the best combination of hybrid energy systems is applied. The micro-grid has been optimized using HOMER (Hybrid Optimization Model for Electric Renewables) software. Results show the Cost of Energy (COE) for different demands and the costs associated to operation. Considering the daily radiation and average wind speed of the site, it was found the substitution of wind turbines by adding more photovoltaic panels with a diesel generator and batteries. Annual dispends can be reduced 54% when the optimal configuration is applied.

https://www.ijrer.com/index.php/ijrer/article/download/4724/pdf

Optimization of a Hybrid Energy System for an Isolated Community in Brazil

In this study, a model for wheeled mobile robots that includes a static friction model in the force balance at the robot's center of mass is presented. Additionally, a least-squares method to linearly combine functions is proposed to estimate the friction coefficients. The experimental and simulation results are discussed to demonstrate the effectiveness of this approach in indoor environments for two floor types.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0263574715000065

Modeling and friction estimation for wheeled omnidirectional mobile robots

This paper seeks to identify, through statistical analysis, the main variables that explain the phenomenon of default on residential electricity customers in the state of Bahia-Brazil. Identification and analysis of these variables is of great importance for building and operating models to support decision-making in favor of fighting delinquency. Database of residential consumers of Electricity Company of Bahia State (Coelba) are used as a study case. The analysis uses the concept of the Pearson correlation coefficient and bivariate method of Principal Component Analysis.

Statistical analysis of the relationship between payment behavior variables and delinquency in electricity consumption

This work proposes the approaches of neural networks and logistic regression for selection of customers with electricity consumption bills late for the proposition of collection actions. Variables determined from business information such as debt amount, overdue invoices and time debt of residential customers of the Company of the State of Bahia Electricity (COELBA) are used in this study. The proposed methodologies were compared with each other and with the actual results, showing improvement in the overall efficiency of collection actions.

Luciana Martinez

Papers

Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot

Optimization of a Hybrid Energy System for an Isolated Community in Brazil

Modeling and friction estimation for wheeled omnidirectional mobile robots

Statistical analysis of the relationship between payment behavior variables and delinquency in electricity consumption

Analysis of the inclusion of quantitative methods for the improvement of the effectiveness of collection actions in a power utility