A Practical Fuzzy Controller with Q-learning Approach for the Path Tracking of a Walking-aid Robot

[[abstract]]This study tackles the path tracking problem of a prototype walking-aid (WAid) robot which features the human-robot interactive navigation. A practical fuzzy controller is proposed for the path tracking control under reinforcement learning ability. The inputs to the designed fuzzy controller are the error distance and the error angle between the current and the desired position and orientation, respectively. The controller outputs are the voltages applied to the left- and right-wheel motors. A heuristic fuzzy control with the Sugeno-type rules is then designed based on a model-free approach. The consequent part of each fuzzy control rule is designed with the aid of Q-learning approach. The design approach of the controller is presented in detail, and effectiveness of the controller is demonstrated by hardware implementation and experimental results under human-robot interaction environment. The results also show that the proposed path tracking control methods can be easily applied in various wheeled mobile robots.[[conferencetype]]國際[[conferencedate]]20140914~20140917[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Nagoya, Japa

Optimizing the performance of a wheeled mobile robots for use in agriculture using a linear-quadratic regulator

Use of wheeled mobile robot systems could be crucial in addressing some of the future issues facing agriculture. However, robot systems on wheels are currently unstable and require a control mechanism to increase stability, resulting in much research requirement to develop an appropriate controller algorithm for wheeled mobile robot systems. Proportional, integral, derivative (PID) controllers are currently widely used for this purpose, but the PID approach is frequently inappropriate due to disruptions or fluctuations in parameters. Other control approaches, such as linear-quadratic regulator (LQR) control, can be used to address some of the issues associated with PID controllers. In this study, a kinematic model of a four-wheel skid-steering mobile robot was developed to test the functionality of LQR control. Three scenarios (control cheap, non -zero state expensive; control expensive, non -zero state cheap; only non -zero state expensive) were examined using the characteristics of the wheeled mobile robot. Peak time, settling time, and rising time for cheap control based on these scenarios was found to be 0.1 s, 7.82 s, and 4.39 s, respectively

Discrete Globalised Dual Heuristic Dynamic Programming in Control of the Two-Wheeled Mobile Robot

Network-based control systems have been emerging technologies in the control of nonlinear systems over the past few years. This paper focuses on the implementation of the approximate dynamic programming algorithm in the network-based tracking control system of the two-wheeled mobile robot, Pioneer 2-DX. The proposed discrete tracking control system consists of the globalised dual heuristic dynamic programming algorithm, the PD controller, the supervisory term, and an additional control signal. The structure of the supervisory term derives from the stability analysis realised using the Lyapunov stability theorem. The globalised dual heuristic dynamic programming algorithm consists of two structures: the actor and the critic, realised in a form of neural networks. The actor generates the suboptimal control law, while the critic evaluates the realised control strategy by approximation of value function from the Bellman’s equation. The presented discrete tracking control system works online, the neural networks’ weights adaptation process is realised in every iteration step, and the neural networks preliminary learning procedure is not required. The performance of the proposed control system was verified by a series of computer simulations and experiments realised using the wheeled mobile robot Pioneer 2-DX

Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening

Adaptive sliding mode control for uncertain wheel mobile robot

In this paper a simple adaptive sliding mode controller is proposed for tracking control of the wheel mobile robot (WMR) systems. The WMR are complicated systems with kinematic and dynamic model so the error dynamic model is built to simplify the mathematical model. The sliding mode control then is designed for this error model with the adaptive law to compensate for the mismatched. The proposed control scheme in this work contains only one control loop so it is simple in both implementation and mathematical calculation. Moreover, the requirement of upper bounds of disturbance that is popular in the sliding mode control is cancelled, so it is convenient for real world applications. Finally, the effectiveness of the presented algorithm is verified through mathematical proof and simulations. The comparison with the existing work is also executed to evaluate the correction of the introduced adaptive sliding mode controller. Thoroughly, the settling time, the peak value, the integral square error of the proposed control scheme reduced about 50% in comparison with the compared disturbance observer based sliding mode control

Robust adaptive controller for wheel mobile robot with disturbances and wheel slips

In this paper an observer based adaptive control algorithm is built for wheel mobile robot (WMR) with considering the system uncertainties, input disturbances, and wheel slips. Firstly, the model of the kinematic and dynamic loops is shown with presence of the disturbances and system uncertainties. Next, the adaptive controller for nonlinear mismatched disturbance systems based on the disturbances observer is presented in detail. The controller includes two parts, the first one is for the stability purpose and the later is for the disturbances compensation. After that this control scheme is applied for both two loops of the system. In this paper, the stability of the closed system which consists of two control loops and the convergence of the observers is mathematically analysed based on the Lyapunov theory. Moreover, the proposed model does not require the complex calculation so it is easy for the implementation. Finally, the simulation model is built for presented method and the existed one to verify the correctness and the effectiveness of the proposed scheme. The simulation results show that the introduced controller gives the good performances even that the desired trajectory is complicated and the working condition is hard