Fuzzy Motion Control Research Articles

The Design and the Development of a Biped Robot Cooperation System

The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together.

Autonomous Underwater Robot Fuzzy Motion Control System with Parametric Uncertainties

The paper describes the design of a fuzzy motion control system of an autonomous underwater vehicle. A mathematical model of the underwater vehicle is synthesized. A fuzzy regulator for controlling the depth of immersion autonomous underwater vehicle is designed. The quality of control for step control, harmonic control, as well as various types of exogenous disturbances, is investigated. The comparison of the functioning quality of the designed fuzzy controller with the proportional–derivative controller is made. It is shown that the designed fuzzy controller provides a higher quality of control compared to the proportional–derivative controller. The proposed fuzzy controller provides high-quality control of the plant under uncertainties.

Double-loop fuzzy motion control with CoG supervisor for two-wheeled self-balancing assistant robots

Though adding manipulator arms gives a two-wheeled self-balancing assistant robot (TSAR) the ability of fetching object, the degenerated balancing performance would occur without considering the effect of robot center-of-gravity (CoG) position when the manipulator arms operate. To tackle this problem, a double-loop fuzzy motion control (DFMC) system is proposed to control the TSAR moving, turning, and reaching a desired position while keeping TSAR balanced. A CoG supervising controller is proposed to control the body pitch angle of TSAR with respect to manipulator arms operate. To show the effectiveness of the CoG supervising controller, three engagement scenarios are applied in the presence of external disturbances and system uncertainties. The experimental results show that the proposed DFMC system with considering CoG supervising controller can achieve better motion performance than without considering that. Further, a visual serving technique is used for doing object recognition and a fuzzy guidance control (FGC) is proposed to let the TSAR can implement the object tracking mission. The experimental results show that not only the DFMC system can maintain TSAR balance but also the FGC system can lead TSAR to fetch target object successfully.

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

In this paper, a new intelligent robot motion control architecture --- a highly accurate model-free fuzzy motion control- is proposed in order to achieve improved robot motion accuracy and dynamic performance. Its architecture combines a Mamdani fuzzy proportional (P) and a conventional integral (I) plus derivative (D) controller for the feedback part of the system, and a Takagi-Sugeno-Kang fuzzy controller for the feed-forward, nonlinear part. The fuzzy P + ID controller improves the performance of the nonlinear system, and the TSK fuzzy controller uses a TSK fuzzy inference system based on extended subtractive- clustering method which integrates information on joint angular displacement, velocity and acceleration for torque identification. The advantage of this kind of model-free control is that it uses the information directly from the input/output of the nonlinear system, without any complex robot model computation, in order to decrease the control system's sensitivity to any dynamical uncertainty. Furthermore, parametric search for clustering parameters in extended subtractive clustering secures the high accuracy of the system identification. Consequently, this proposed model-free fuzzy motion control benefits from the advantages of two kinds of fuzzy system. It not only incorporates flexible design, good performance and simple conception but also ensures precise motion control and great robustness. Comparisons with other intelligent models and results from numerical studies on a 4-bar planar parallel mechanism show the effectiveness and competitiveness of the proposed control.

Dynamic Balance Control for Biped Robot Walking Using Sensor Fusion, Kalman Filter, and Fuzzy Logic

The walking ability is a fundamental research for biped robots, and there always needs to be a criterion used for judging the walking stability. The zero moment point (ZMP) criterion is one of the useful standards for measuring biped robot walking. Here, a new ZMP trajectory model with adjustable parameters is proposed to modulate the ZMP trajectory both in sagittal and lateral planes and make the ZMP trajectory more flexible. A dynamic balance control (DBC), which includes Kalman filter (KF) and the fuzzy motion controller (FMC), is also designed to keep the body balance and make the biped walking following the desired ZMP reference. In addition, KF is utilized to estimate the system states and reduce the effect caused by noise. Using sensor fusion technique, ZMP error and trunk inclination measured by the force sensor and accelerometer are served as the inputs for FMC, which is presented to correct each joint of the biped robot dynamically. When a biped robot walks under different ground conditions, the coordination of the designed ZMP trajectory and proposed DBC can achieve a successful biped walking. Finally, there are several experiments presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

Walking Motion Generation, Synthesis, and Control for Biped Robot by Using PGRL, LPI, and Fuzzy Logic

This paper proposes the implementation of fuzzy motion control based on reinforcement learning (RL) and Lagrange polynomial interpolation (LPI) for gait synthesis of biped robots. First, the procedure of a walking gait is redefined into three states, and the parameters of this designed walking gait are determined. Then, the machine learning approach applied to adjusting the walking parameters is policy gradient RL (PGRL), which can execute real-time performance and directly modify the policy without calculating the dynamic function. Given a parameterized walking motion designed for biped robots, the PGRL algorithm automatically searches the set of possible parameters and finds the fastest possible walking motion. The reward function mainly considered is first the walking speed, which can be estimated from the vision system. However, the experiment illustrates that there are some stability problems in this kind of learning process. To solve these problems, the desired zero moment point trajectory is added to the reward function. The results show that the robot not only has more stable walking but also increases its walking speed after learning. This is more effective and attractive than manual trial-and-error tuning. LPI, moreover, is employed to transform the existing motions to the motion which has a revised angle determined by the fuzzy motion controller. Then, the biped robot can continuously walk in any desired direction through this fuzzy motion control. Finally, the fuzzy-based gait synthesis control is demonstrated by tasks and point- and line-target tracking. The experiments show the feasibility and effectiveness of gait learning with PGRL and the practicability of the proposed fuzzy motion control scheme.

Fuzzy/Kalman Hierarchical Horizontal Motion Control of Underactuated ROVs

A new closed loop fuzzy motion control system including on-line Kalman's filter (KF) for the two dimensional motion of underactuated and underwater Remotely Operated Vehicle (ROV) is presented. Since the sway force is unactuated, new continuous and discrete time models are developed using a polar transformation. A new hierarchical control architecture is developed, where the high level fuzzy guidance controller generates the surge speed and the yaw rate needed to achieve the objective of planar motion, while the low level controller gives the thruster surge force and the yaw torque control signals. The Fuzzy controller ensures robustness with respect to uncertainties due to the marine environment, forward surge speed and saturation of the control signals. Also Lyapunov's stability of the motion errors is proved based on the properties of the fuzzy maps. If Inertial Measurement Unit data (IMU) is employed for the feedback directly, aleatory noises due to accelerometers and gyros damage the performances of the motion control. These noises denote a kind of non parametric uncertainty which perturbs the model of the ROV. Therefore a KF is inserted in the feedback of the control system to compensate for the above uncertainties and estimate the feedback signals with more precision.

Fuzzy motion control strategy for cooperation of multiple automated vehicles with passengers comfort

This paper considers motion control for a cooperative system of automated passenger vehicles. It develops a cooperative scheme based on a decentralized planning algorithm which considers the vehicles in an initial open chain configuration. In this scheme the trajectories are intersections-free, and each trajectory is planned independently of the others. To ensure the stabilization of each vehicle in the planned trajectory, a fuzzy closed loop motion control is presented, where, based on the properties of the Fuzzy maps, the Lyapunov’s stability of the motion errors is demonstrated for all the vehicles. Based on the ISO 2631-1 standard, the saturation property of the Fuzzy maps guarantees low values of longitudinal and lateral accelerations, to ensure comfort of the human body during the motion. The validity of this control algorithm is supported by simulation experiments.

Study on the Compound Fuzzy Motion Control for the Ultra Precision Machine Tools

This paper presents a sophisticated trajectory tracking algorithm based on the utilization of the compound control and fuzzy contour control for the ultra precision machine tools. The main idea is to use the compound control to broaden the bandwidth of the servo system and, therefore, improve the tracking accuracy of the single axis. Then, FUZZY cross coupled contour control is introduced to compensate for the contour error caused by imperfect dynamics of the tracking axis and the mismatch of the control parameters of the servo systems. Experiments carried out on the HCM-1 ultra precision machine tool with a T-type layout show that the integration of the compound control with the fuzzy cross coupled contour control can effectively improve the contour tracking accuracy of the ultra precision machine tool.

AN EMBEDDED GENETIC FUZZY MOTION CONTROLLER FOR A MOBILE ROBOT

In this paper, an embedded genetic fuzzy motion control system is developed for autonomous navigation and obstacle avoidance of a mobile robot built in the ARIS Lab. A combination of four infrared sensors is equipped to measure the distance to obstacles around the mobile robot. The distance information is processed by the proposed genetic fuzzy controller to adjust the velocities of two separately-driven wheels of the robot. Here the fuzzy logic is used to process the sensor information generating the control commends; and the genetic algorithm is exploited to tune the membership functions of the fuzzy rules. Local search techniques are utilised to enhance the tuning process. The proposed motion controller has been applied on the mobile robot. Experiments have demonstrated the effectiveness of the developed genetic fuzzy controller.

Fuzzy Controller for Wall-Climbing Microrobots

This paper presents a fuzzy control system that incorporates sensing, control and planning to improve the performance of the wall-climbing microrobots in unstructured environments. After introduction of the robot system, a task reference method is proposed which is based on a fuzzy multisensor data fusion scheme. The method provides a novel mechanism to efficiently integrate task scheduling, action planning and motion control in a unified framework. A robot gait generation method is described which switches the robot locomotion between different motion modes with the help of a finite state machine driven by sensory information. A fuzzy motion controller is designed to improve control performance and reduce power consumption by the suitable selection of fuzzy sets and inference methods, as well as the definition of corresponding membership functions and control rule bases. A fuzzy logic compensator is developed to compensate the gravitational effects according to different robot configurations and task situations. Experimental results prove the validity of the proposed methods.

Fuzzy Sensor-Based Motion Control among Dynamic Obstacles for Intelligent Rigid-Link Electrically Driven Arm Manipulators

An intelligent motion planning based on fuzzy rules for the idea of artificial potential fields using analytic harmonic functions is presented. The purpose of the combination of a fuzzy controller and a robust controller is to design a realistic controller for nonlinear electromechanical systems such as an electric motor actuating an arm robot. This control algorithm is applied to the three basic navigation problems of intelligent robot systems in unstructured environments: autonomous planning, fast nonstop navigation without collision with obstacles, and dealing with structured and/or unstructured uncertainties. To achieve this degree of independence, the robot system needs a variety of sensors to be able to interact with the real world. Sonar range data is used to build a description of the robot's surroundings. The proposed approach is simple, computationally fast, and applies to whole-arm collision avoidance. The stability of the overall closed loop system is guaranteed by the Lyapunov theory. Simulation results are provided to validate the theoretical concepts, and a comparative analysis demonstrates the benefits of the proposed obstacle avoidance algorithm.

Robust fuzzy and recurrent neural network motion control among dynamic obstacles for robot manipulators

An integration of a fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The navigation technique of robot control using artificial potential fields is based on the fuzzy controller. The NN controller can deal with unmodeled bounded disturbances and or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned online, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.

Fuzzy motion control of an auto-warehousing crane system

Fuzzy motion control of an auto-warehousing crane system is presented in this paper. Using the concept of linguistic variable, a fuzzy logic controller (FLC) can convert the knowledge and experience of an expert into an automatic control strategy. The designed FLC with a rule base and three sets of parameters is used to control the crane system in x, y, and z directions. The unloaded weight and the fully loaded weight of the crane system in discussion are 1.35/spl times/10/sup 4/ kg and 1.5/spl times/10/sup 4/ kg, respectively. For various loading conditions and varying distances, the FLC still controls the crane system very well with positioning accuracy less than 2/spl times/10/sup -3/ m for all directions. The distance-speed reference curve for control of the crane system is designed to meet the engineering specifications of motion such as acceleration, deceleration, maximum speed, and creep speed in each direction, and is generated automatically according to varying distance. The method for designing the distance-speed reference curve can make the crane move at relatively high speed to approach the target position. Simulations of the motion control in the three directions are demonstrated.

Swing-Up Trajectory Learning and Motion Control of an Inverted Pendulum Control Robot by Fuzzy Logic.

This paper describes the swing-up trajectory learning and motion control of an inverted pendulum control robot based on fuzzy logic. The learning algorithm obtaining the optimal swing-up trajectory of the inverted pendulum is constructed based on fuzzy logic. From the experimental results, it is confirmed that the optimal swing-up trajectory can be obtained by the present fuzzy learning algorithm. The swing-up and stabilizing fuzzy control system is constructed with the variable-gain fuzzy logic controller and the velocity feedback loops. Then the experimental results for the swing-up and stabilizing fuzzy control are shown, and the excellent performance of the present fuzzy motion control system is clarified. Furthermore, a numerical simulation has been carried out, and the numerical simulation results agree well with the experimental results.

Fuzzy rule-based motion controller for an autonomous mobile robot

SUMMARYIntelligent control of mobile systems allows for hierarchical structures that utilize sensory data with various levels of accuracy. This paper discusses a rule-based approach for the control problem. The assumed inexactness in world description is represented by fuzzy memberships, and the state space is discretized into a linguistic vocabulary. Fuzzy motion control rules that have been experimentally derived, are then used in a fuzzy inference mechanism to give the final control command to robot actuators. Finally, the developed algorithm is tested for real-time control applications.

Knowledge Based Fuzzy Motion Control of Autonomous Vehicles

An intelligent mobile land based autonomous vehicle can be modelled as a hierarchy of multi-sensor data fusion, scene recognition, path planning, navigation and motion control. This paper is directed towards the motion control level in developing rule based fuzzy logic controllers that are self-adaptive to substantial changes in plant parameters or due to inadequacies in physical modelling It is shown that a land based vehicle, and its guidance and control, can be modelled as a series of connected linear second order systems for small perturbations in time and motion. Such models and associated control laws are inadequate for motion in unstructured environments or for large slew angular movements. By utilising a fuzzy decision/control algorithm throuqh a fuzzy based production system, it is shown that effective real time lateral motion control is achievable for a wide range of plant parameters/models. Computational aspects of sample rates, number of operations and storage requirements for a reconfigurable rule based fuzzy logic controller are also considered.

Knowledge-based fuzzy motion control of autonomous vehicles

A multi-sensor and multi-parameter condition monitoring and fault diagnosis system is designed and implemented for modern manufacturing systems, such as flexible manufacturing cells and systems. The overall hardware and software designs of this system, together with the functional sub-systems, are presented. This implemented system monitors power, vibration, temperature and pressure of the drives and spindles with a total of 72 diagnostic features and uses a cost-weighted function to identify diagnostic solutions with the lowest cost. Condition monitoring, data acquisition, knowledge acquisition and representation, as well as the diagnostic reasoning strategy of the knowledge-base system are presented in detail. The system was implemented and tested in a textile plant with satisfactory results.