Wheeled mobile robot design with robustness properties

In this paper, two knowledge based controllers are proposed to overcome the difficulties of a computed torque nonlinear controller (NC) in perfect trajectory tracking of nonholonomic wheeled mobile robots (WMRs). First, the effects of different dynamic models developed in angular andCartesian coordinate systems are fully examined on the persistent excitation condition and consequently on the trajectory tracking performance of WMRs. Using the dynamic model coordinated in theCartesian frame as the base of theNCresults in perfect compensation of large position off‐tracks and unbiased estimation of the plant's unknown parameters. However, using theWMR's dynamic model with rotation angles of driving wheels as the base of nonlinear and fuzzy controllers leads to accurate orientation tracking. Through replacing the proportional and differential terms of theNCby fuzzy functions, a fuzzy nonlinear controller (FNC) is generated. Due to the complicated dynamics of the WMR in which the center of mass does not coincide with the center of rotation, the expert knowledge of fuzzy controllers is extracted considering the rotation angles and rates of driving wheels as input variables. Fuzzy tuning of theNCresults in a superior tracking performance against measurement noises, though the control torques are decreased and smoothed significantly. Second, a complete fuzzy controller (FC) is generated to make perfect tracking of theWMR's position and orientation. The local stability analysis of fuzzy controllers is examined considering the corresponding analytical structures as nonlinear controllers. The superior performances of the proposed fuzzy controllers compared to those of theNCs are evaluated through simulations.

In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach.

Many researchers have become interested in wheeled mobile robot (WMR) trajectory tracking control in recent years. This is due to the increased application of mobile robots in the industry, the military, the home, and public service. Classically, the movement of WMR is controlled depending on its kinematic model. However, in real-time applications, both the dynamic and kinematic models of robots and external disturbance and uncertainty affect system performance. This paper proposes backstepping combined with a Nonlinear Proportional-Integral-Derivative (NPID) controller to control a two-wheeled mobile robot (TWMR). The kinematic and dynamic models of the WMR are derived. The dynamic modeling is derived using a Lagrangian approach, and stability of the system is achieved using the Lyapunov method. The controller gains are optimized using the Genetic Algorithm optimization technique. The proposed algorithms’ performance is tested using Matlab software. The simulation result shows that the proposed method achieved preferable reference trajectory tracking with a minimum tracking error. The proposed controller outperforms the GA-based backstepping plus PID controller in terms of root-mean-square (RMS) of trajectory tracking error (47.36% in a linear and 60.32% in a nonlinear case). In addition, it shows good unknown disturbance rejection and initial point change in all scenarios.

Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

This paper addresses the challenge of bidirectional trajectory tracking, for the first time in literature, in wheeled mobile robots (WMRs) powered by renewable energy by thoroughly considering all involved subsystems. A comprehensive robust multilevel average controller is developed, utilizing the mathematical models of the WMR's mechanical structure, actuators, and power stage. At the mechanical-level, a kinematic control is implemented to ensure precise trajectory tracking. The actuator-level control incorporates two controllers based on differential flatness theory, managing the dynamic behavior of the actuators to achieve smooth and efficient motion. Finally, the power-stage-level employs two additional controllers, also based on flatness theory, dedicated to the power stage subsystem. The effectiveness of the proposed robust controller is experimentally validated using: A differential WMR prototype and a BK Precision PVS60085MR for replicating the characteristics of a commercial photovoltaic (PV) panel. System integration is facilitated by the DS1104 control board and implemented using MATLAB/Simulink software for real-time operation. The experimental results demonstrate the multilevel average controller's robustness and reliability, highlighting its capacity to maintain precise bidirectional trajectory tracking even in the presence of sudden changes in system parameters. • Bidirectional trajectory tracking in solar WMRs, considering all subsystems. • Use of full-bridge Buck inverters in the power stage to drive the actuator subsystem. • Validation on a WMR using Bézier polynomial-based bidirectional trajectories. • Experimental verification of the controller under disturbances in WMR's parameters. • Controller evaluation against a three-level strategy reported in the literature.

Robust Nonlinear EGR and VGT Control with Integral Action for Diesel Engines

PurposeThe aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot.Design/methodology/approachFuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot’s trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy–Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot’s speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection.FindingsCompared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR.Practical implicationsThe proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles.Originality/valueA robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).

This study deals with a nonlinear constrained control system for the trajectory tracking of wheeled mobile robots (WMR) with constraints. To this aim, a new constrained controller is developed for WMR based on a predictive approach with input limitations for trajectory tracking. The proposed method leads to an optimization problem that is solved by the Karush Kuhn Tucker conditions (KKT) which employ a continuous nonlinear model of WMR. Finally, the performance of the proposed control method is evaluated by realistic motion models within a virtual robot experimentation platform (V-Rep). The results indicate that the proposed control method not only decreases the trajectory tracking error in the presence of input constraints but also it is suitable for online implementation compared with conventional nonlinear model predictive control (NMPC).

In this study, model predictive control with softening constraints is applied to a non‐holonomic wheeled mobile robot for trajectory tracking in the presence of external disturbances. In order to improve real‐time robustness of the wheeled mobile robot, a linearised tracking error model is used to predict system behaviours. The proposed control scheme contains a feedforward controller and a feedback controller, in which both control constraints and control increment constraints are considered to achieve trajectory tracking smoothly. Finally, numerical simulations demonstrate the performances of the control scheme.

This paper presents a value iteration based approximate dynamic programming technique to solve the trajectory tracking problem of unicycle like wheeled mobile robots. Given a reference trajectory (2D), an error model is derived to form a nonlinear affine system. The robot is supposed to track the reference trajectory asymptotically. The solution of the error model is used to define the value function (cost-to-go function), which is a measure of the robot's tracking error and the cost of applying its actuator inputs (in this case, linear and angular velocities of the robot). A critic neural network approximates the value function to determine the optimal control inputs that are applied to the robot's actuators. A set of computer simulations is conducted to evaluate the performance of the proposed approximate dynamic programming method. The robot's trajectory tracking performance using the proposed method is also compared with that of the conventional approximate linearization technique in order to show the superiority of the value iteration based approximate dynamic programming.

This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of 0.078769, 0.12086 and 0.1672. On sinusoidal trajectories, the method yields RMSE values of 0.01233, 0.015138 and 0.088707, and on sinusoidal with perturbation, RMSE values are 0.021495, 0.016504 and 0.090142 in x, y and θ, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

This study entailed applying image-processing techniques and an intelligent control method to a wheeled mobile robot (WMR) for real-time trajectory recognition and tracking. The WMR was fitted with a charge-coupled device (CCD) camera to capture images of the environment, and the hue-saturation-intensity color space was used to classify the color features of the captured images. The WMR calculates the relative position of a target object by using image-processing and edge-detection algorithms. This paper proposes a cerebellar model articulation controller (CMAC) and adaptive CMAC to replace conventional proportional-integral-derivative (PID) controller for guiding the WMR to track a desired path. Moreover, using Lyapunov's stability theory to optimize the learning rate ensures that the proposed control system is stable. The tracking performance of several controllers used to track various path patterns were compared. Finally, according to the theoretical analysis, simulations, and experimental results of this study, the proposed control scheme is effective.

This article studies a path-following problem for a wheeled mobile robot with nonholonomic constraints. The path-following task is represented by a guidance vector field (GVF), for which an online optimization procedure is adopted to estimate the path error. By exploiting a matrix-measure-based contraction principle, the convergence property of the designed GVF with respect to the task path is theoretically guaranteed. Then, a nonlinear controller is developed to track the defined GVF such that the target path is followed by the controlled mobile robot in the presence of unknown disturbances, including the unmodeled dynamics and the surface friction. Robustness properties of the closed-loop system are analyzed, and it is shown that the path error eventually converges to a residual set, which can be reduced by increasing control gains. Experiments are provided to validate the effectiveness of the desired GVF and the proposed control design.

This work describes the study of modeling and controller on wheeled mobile robots designed the motors which is driving the wheels. According to the structure and design of wheeled mobile robot, DC motors are the best suited for the motion control. The kinematical model is required for the designing process of the wheels in the WMR. The analysis of the mathematical model is divided into angle and velocity of the dc motor build in wheeled mobile robot because of the importance of motor parameters for stability. The main focus of the work is to develop an efficient controller to control the speed of the dc motor applied in the wheels of the robot. PID tuning has been implemented in designing of the controller for the speed control of dc motor. The open loop and closed loop performance of a two wheeled mobile robot with PID and LQR controllers are obtained and compared by using MATLAB programs and simulations.

In this brief, a novel robust nonlinear motion controller with disturbance observer (DOB) for positioning control of a nonlinear single-input-single-output (SISO) mechanical system is proposed. The controller is designed in a backstepping manner. First, a proportional-integral (PI) controller is designed to stabilize the position error. Consequently, a novel robust nonlinear velocity controller with DOB is designed to stabilize the velocity error. With the help of nonlinear damping terms, the input-to-state stability (ISS) property of the overall nonlinear control system is proven, which leads to a major contribution of construction of a theoretically guaranteed robust nonlinear controller with DOB. The performance of the proposed controller is verified through application to a magnetic levitation system. Comparative studies with an adaptive robust nonlinear controller are also carried out. It is shown that the proposed novel controller while being simple is superior over the adaptive robust nonlinear controller for the experimental setup under study.