In this article, we propose a new scheme for a fuzzy logic controller, which includes acceleration as one of its linguistic variables, as opposed to other techniques and approaches that have been developed and reported in the literature. This method is used for controlling the tracking of the trajectory followed by the joints of a 2-DoF manipulator robot. To this end, a complete simulation environment is developed through the MatLab/Simulink® software. The dynamic model of the manipulator robot includes a vector that consists of the estimate of the friction forces present in the joints. Then, a controller based on fuzzy logic is designed and implemented for each joint. Finally, the performance of the developed system is assessed and then compared to the performance of a classic PID controller. The incorporation of the fuzzy variable acceleration significantly improved the system’s response.

Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy-real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot's sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications.

Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges. Our recently proposed model-learning orientation controller has shown promising ability to simultaneously control a three-degrees-of-freedom robot manipulator’s end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot’s serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times, and it was found that, in 82% of the times, the robot successfully reached the desired target pose within 150 steps.

Kinematic-Model-Free Redundancy Resolution Using Multi-Point Tracking and Control for Robot Manipulation

This study intends to address the platoon formation control problem of a team of N electrically driven underactuated autonomous car-like mobile robots. A platoon controller is proposed by using the...

Observer-based robust platoon formation control of electrically driven car-like mobile robots under collision avoidance and connectivity maintenance with a prescribed performance:

Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as robot’s own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free approaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed according to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control.

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Model-based and Model-Free Robot Control : A Review

Model-free control of electrically driven robot manipulators using an extended state observer

This paper presents a new approach for robot manipulator control, based on voltage control strategy. The novelty of this paper is providing a model-free control system with optimized parameters. In this approach, it is not necessary to have dynamic equations and parameters of robot and its motors. Uncertainties and disturbances of each joint of the robot manipulator is estimated by a Linear Extended State Observer (LESO), and the convergence of estimated variables to actual variables has been analyzed. After estimating the disturbances, a proportional controller is used to tracking the reference trajectory. LESO and controller design parameters have been determined using the PSO algorithm. Simulation results depicted that the proposed model-free controller has a satisfactory performance for reducing the position tracking error.

Model Free Control of Electrically Driven Manipulator by Optimized Linear Extended State Observer, based on Voltage Control Strategy

This paper presents a Model-Free approach for robot manipulators control. The control signal in this approach is the motors' voltage, and can be directly used. For each of the manipulator joints, a local model is considered, which is based on the manipulator's input and output, and each joint is controlled independently. The only information needed for implementation of this method is the motors' voltage and the position of the joints, which are both easily measurable. In this approach, all system dynamics and external disturbances are considered as an unknown term, that is estimated using an adaptive law obtained from the Lyapunov theory. The designed control law is simple, and controller stability is guaranteed by using this theory. Simulation results and comparison verify the effectiveness of the proposed approach.

Amir Saleki

Papers

Model-free control of electrically driven robot manipulators using an extended state observer

Model Free Control of Electrically Driven Manipulator by Optimized Linear Extended State Observer, based on Voltage Control Strategy

Adaptive Model-Free Control of Electrically Driven Robot Manipulators