Control Of Mobile Robot System Research Articles

Trajectory Tracking of Mobile Robot Based on Improved Hierarchical Sliding Mode Control

A control algorithm based on PD control and non-singular terminal hierarchical sliding mode control is proposed for tracking control of mobile robot systems. First establish a dynamic model of mobile robot under cartesian coordinate system, the system can be divided into subsystems of posture and position, attitude subsystem by using PD control, location subsystem using non-singular terminal hierarchical sliding mode control, the introduction of the system state higher order term, improve the system in the convergence speed and stability of the sliding surface, the system reaches a steady state in a limited time; Finally, the stability of the system is proved theoretically, and the effectiveness of the control method is verified by simulation.

Finite Horizon Robust Nonlinear Model Predictive Control for Wheeled Mobile Robots

The control of mobile robotic systems with input constraints is still a remarkable problem for many applications. This paper studies the model predictive control-based kinematic control scheme after implementing the decoupling technique of wheeled mobile robots (WMRs). This method enables us to obtain the easier optimization problem with fixed initial state. The finite horizon in cost function of model predictive control (MPC) algorithm requires the appropriate terminal controller as well as the equivalent terminal region. The stability of MPC is determined by feasible control sequence. Finally, offline simulation results validate that the computation load is significantly reduced and also validate trajectory tracking control effectiveness of our proposed control scheme.

Formation control of mobile robot systems incorporating primal-dual neural network and distributed predictive approach

This paper addresses the formation problem for multiple mobile robots with velocity mismatch and system constraints by a distributed model predictive control (DMPC) strategy and a modified virtual structure method. Firstly, a desired virtual structure is employed to generate a set of reference paths for the formation robots. By including approaching angle and path parameter synchronization constraints into cost function, a Nash-based DMPC strategy is presented, where a velocity integral controller is developed to solve the velocity mismatch. Further, consider state and input constraints, the distributed optimization problem is rewritten as a constrained quadratic programming (QP) problem. A PDNN is used to obtain the optimal control input increments, and the stability of the proposed algorithm is analyzed. Moreover, the dynamic formation control is achieved by a modified virtual structure method, and simulation examples are given to verified the effectiveness of the proposed strategies.

Finite-time control of mobile robot systems with unmeasurable angular and linear velocities via bioinspired neurodynamics approach

This paper addresses the stability analysis and adaptive robust finite-time bioinspired neurodynamics control for a class of mobile robot systems with unmeasurable angular and linear velocities, and time-varying bounded disturbance. The error system of the mobile robot is decomposed into two subsystems based on the system model. The state feedback control laws with observers are designed for the two subsystems, and the adaptive robust finite-time bioinspired neurodynamics controller (ARFBNC) is designed based on the state feedback control laws and two subsystems. The stability conditions in the form of linear matrix inequalities (LMIs) are derived by introducing the Lyapunov–Krasovskii functional. The unmeasurable angular and linear velocities, and time-varying bounded disturbance are estimated effectively by employing the state feedback control laws with observers. The smooth bounded outputs are obtained and the sharp jumps of initial values for the state variables are reduced. The closed-loop system is asymptotically stable and the state errors converge to an adjustable bounded region by introducing the Lyapunov–Krasovskii functional. The simulations are performed to show the effectiveness of the proposed methods.

Adaptive robust finite-time control of mobile robot systems with unmeasurable angular velocity via bioinspired neurodynamics approach

This paper addresses the adaptive robust finite-time bioinspired neurodynamics control (ARFBNC) for a class of mobile robots with unmeasurable angular velocity and multiple time-varying bounded disturbances. The error system of the mobile robot is decomposed into two subsystems based on the system model. The state feedback control laws with disturbance feed-forward compensators are designed for the two subsystems, respectively. The ARFBNC method is designed based on the state feedback control laws and two subsystems. The stability conditions in the form of linear matrix inequalities (LMIs) are derived by introducing the Lyapunov–Krasovskii functional. Unlike other works, both the unmeasurable angular velocity and multiple time-varying bounded disturbances are estimated effectively. The smooth bounded outputs are obtained and the sharp jumps of initial values for the state errors are reduced. By introducing the Lyapunov–Krasovskii functional, the closed-loop system is asymptotically stable and the state errors converge to an adjustable bounded region. Finally, three examples are given to show the effectiveness and advantage of the proposed methods.

Control of a non-holonomic mobile robot system with parametric uncertainty

In this paper, the control of a mobile robot system via a feedback linearization controller and anti-control of chaos with parametric uncertainty is researched. Anti-control is also applied to convert non-chaotic systems to chaotic ones and to create chaos dynamic. The synchronization of system errors with a chaotic gyroscope system is researched for energy reduction and performance improvement. In the other words, control effort is based on synchronizing the error system with chaos for decreasing control cost. The combination of these techniques yields high efficiency and global convergence of trajectories, even in the presence of parametric uncertainty, which has been shown by simulation. Finally, the energy of control signals is calculated and compared for showing the energy reduction.

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

SUMMARYModel predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

A Modification Artificial Bee Colony Algorithm for Optimization Problems

This paper presents a modified artificial bee colony algorithm (MABC) for solving function optimization problems and control of mobile robot system. Several strategies are adopted to enhance the performance and reduce the computational effort of traditional artificial bee colony algorithm, such as elite, solution sharing, instant update, cooperative strategy, and population manager. The elite individuals are selected as onlooker bees for preserving good evolution, and, then, onlooker bees, employed bees, and scout bees are operated. The solution sharing strategy provides a proper direction for searching, and the instant update strategy provides the newest information for other individuals; the cooperative strategy improves the performance for high-dimensional problems. In addition, the population manager is proposed to adjust population size adaptively according to the evolution situation. Finally, simulation results for optimization of test functions and tracking control of mobile robot system are introduced to show the effectiveness and performance of the proposed approach.

Decentralized connectivity maintenance for cooperative control of mobile robotic systems

To accomplish cooperative tasks, robotic systems are often required to communicate with each other. Thus, maintaining connectivity of the communication graph is a fundamental issue in the field of multi-robot systems. In this paper we present a completely decentralized control strategy for global connectivity maintenance of the communication graph. Considering the disk communication model, we describe a gradient-based control strategy that exploits decentralized estimation of the algebraic connectivity. Unlike previous approaches available in the literature, the proposed control algorithm solves the global connectivity problem in a decentralized manner providing theoretical guarantees, without requiring maintenance of the local connectivity between robotic systems. Moreover, results obtained with simulations and experiments on real robots are described for demonstrating the efficacy of the proposed algorithm.

Vision-Based Motion Control of Mobile Robotic Systems for Human Support

Vision-Based Motion Control of Mobile Robotic Systems for Human Support