Parameter-Adaptive Event-Triggered Sliding Mode Control for a Mobile Robot

Mobile robots have played a vital role in the transportation industries, service robotics, and autonomous vehicles over the past decades. The development of robust tracking controllers has made mobile robots a powerful tool that can replace humans in industrial work. However, most of the traditional controller updates are time-based and triggered at every predetermined time interval, which requires high communication bandwidth. Therefore, an event-triggered control scheme is essential to release the redundant data transmission. This paper presents a novel parameter-adaptive event-trigger sliding mode to control a two-wheeled mobile robot. The adaptive control scheme ensures that the mobile robot system can be controlled accurately without the knowledge of physical parameters. Meanwhile, the event trigger sliding approach guarantees the system robustness and reduces resource usage. A simulation in MATLAB and an experiment are carried out to validate the efficiency of the proposed controller.

Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

There is a high demand for developing effective controllers to perform fast and accurate operations for either flexible link manipulators (FLMs) or rigid link manipulators (RLMs). Thus, this paper is beneficial for such vast field, and it is also advantageous and indispensable for researchers who are interested in robotics to have sufficient knowledge about various controllers of FLMs and RLMs as the controllers’ concepts are elaborated in detail. The paper concentrates in critically reviewing classical controllers, intelligent controllers, robust controllers, and hybrid controllers for both FLMs and RLMs. The advantages and disadvantages of the aforementioned control methods are summarized in this paper; it also has a detailed comparison for the controllers in terms of the design difficulty, performance, and the suitability for controlling FLMs or RLMs.

Dedicated Nonlinear Control of Robot Manipulators in the Presence of External Vibration and Uncertain Payload

Robot manipulators are often tasked with working in environments with vibrations and are subject to load uncertainty. Providing an accurate tracking control design with implementable torque input for these robots is a complex topic. This paper presents two approaches to solve this problem. The approaches consider joint space tracking control design in the presence of nonlinear uncertain torques caused by external vibration and payload variation. The properties of the uncertain torques are used in both approaches. The first approach is based on the boundedness property, while the second approach considers the differentiability and boundedness together. The controllers derived from each approach differ from the perspectives of accuracy, control effort, and disturbance properties. A Lyapunov-based analysis is utilized to guarantee the stability of the control design in each case. Simulation results validate the approaches and demonstrate the performance of the controllers. The derived controllers show stable results at the cost of the mentioned properties.

Optimal adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control method for some classes of nonlinear systems

In this research, a novel adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control (AIT2FFOBSMC) method is presented for some classes of nonlinear fully-actuated and under-actuated mechanical systems with uncertainty. The AIT2FFOBSMC method exploits the advantages of backstepping and sliding mode methods to improve the performance of closed-loop control systems by lowering the tracking error and increasing robustness. To mitigate chattering and the tracking error, a fractional sliding surface is designed. In addition to the fractional sliding surface, an adaptive interval type-2 fuzzy compensator is used to estimate the uncertainty and perturbation of the nonlinear system in order to further reduce chattering caused by switching term as well as to enhance the perturbation rejection. In order to achieve an optimal performance, the multi-tracker optimization algorithm (MTOA) is used. Finally, a number of simulations and experimental tests are carried out to examine the performance of the AIT2FFOBSMC method.