Min Cheol Lee

Sliding mode controller with sliding perturbation observer based on gain optimization using genetic algorithm

The Stewart platform manipulator is a closed-kinematics chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. However, this is a complex and nonlinear system, so the control performance of the system is not so good. In this paper, a new robust motion control algorithm is proposed. The algorithm uses partial state feedback for a class of nonlinear systems with modeling uncertainties and external disturbances. The major contribution is the design of a robust observer for the state and the perturbation of the Stewart platform, which is combined with a variable structure controller (VSC). The combination of controller and observer provides the robust routine called sliding mode control with sliding perturbation observer (SMCSPO). The optimal gains of SMCSPO, which is determined by nominal eigenvalues, are easily obtained by genetic algorithm. The proposed fitness function that evaluates the gain optimization is to put sliding function. The control performance of the proposed algorithm is evaluated by the simulation and experiment to apply to the Stewart platform. The results showed high accuracy and good performance.

Improving tracking performance of industrial SCARA robots using a new sliding mode control algorithm

This paper addresses the implementation of a new sliding mode control algorithm for high speed and high precision tasks, which is robust against variations in the robot parameters and load. The effects of nonlinear dynamics, which are difficult to model accurately, become prominent in high speed operations. This paper attempts to treat the nonlinear dynamics of a SCARA robot as a disturbance. Based upon this approach, a new sliding mode control algorithm is proposed, in which a switching control input can be obtained easily and is determined to satisfy the existence condition for sliding mode control. A graphic simulator is used to evaluate the proposed algorithm for a SCARA robot. Simulation results show that the proposed algorithm is robust against disturbances and can reduce the magnitude of chattering, which is an unavoidable problem in sliding mode control. Experiments are carried out to validate the simulated results with an industrial SCARA robot using DSPs.

A New Technique to Escape Local Minimum in Artificial Potential Field Based Path Planning

The artificial potential field (APF) methods provide simple and efficient motion planners for practical purposes. However, these methods have a local minimum problem, which can trap an object before reaching its goal. The local minimum problem is sometimes inevitable when an object moves in unknown environments, because the object cannot predict local minima before it detects obstacles forming the local minima. The avoidance of local minima has been an active research topic in the potential field based path planing. In this study, we propose a new concept using a virtual obstacle to escape local minima that occur in local path planning. A virtual obstacle is located around local minima to repel an object from local minima. We also propose the discrete modeling method for the modeling of arbitrary shaped objects used in this approach. This modeling method is adaptable for real-time path planning because it is reliable and provides lower complexity.

Development of a washout algorithm for a vehicle driving simulator using new tilt coordination and return mode

A vehicle driving simulator is a virtual reality device which makes a man feel as if he drove art actual vehicle Unlike actual vehicles, the simulator has limited kinematical workspace and bounded dynamic characteristics So it is difficult to simulate dynamic motions of a multi-body vehicle model In order to overcome these problems, a washout algorithm which controls the workspace of the simulator within the kinematical limitation is needed However, a classical washout algorithm contains several problems such as generation of wrong sensation of motions by filters in tilt coordination, requirement of trial and error method in selecting the proper cutoff frequencies and difficulty in returning the simulator to its origin using only high pass filters This paper proposes a washout algorithm with new tilt coordination method which gives more accurate sensations to drivers To reduce the time in returning the simulator to its ougin, an algonthrn that applies selectively onset mode from high pass filters and return mode from error functions is proposed As a result of this study, the results of the proposed algorithm are compared with the results of classical washout algorithm through the human perception models Also, the performance of the suggested algorithm is evaluated by using human perception and sensibility of some drivers through experiments

Sliding mode control with fuzzy adaptive perturbation compensator for 6-DOF parallel manipulator

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator (FAPC) to get a good control performance and reduce the chatter. The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network (FAN). The weighting parameters of the compensator are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

Implementation of a new sliding mode control for SCARA robot

This paper addresses the implementation of a new sliding mode control algorithm for a high speed/precision controller which is robust against the variation of robot parameters and load. The nonlinear dynamics become prominent in high speed operation but can hardly be modeled accurately. Therefore, we consider the nonlinear inertia, Coriolis and centrifugal force terms as external disturbances. This approach is applied to sliding mode control to obtain switching control inputs. The control input obtained satisfy the existence condition of sliding mode. A dynamic simulator has been developed and used to evaluate the proposed algorithm for SCARA robots. Simulated results show that the proposed algorithm is robust against disturbances and reduces the amount of chattering. An experiment was performed to demonstrate the simulated results in a real system.

Identification of motion platform using the signal compression method with pre-processor and its application to siding mode control

In case of a single input single output (SISO) system with a nonlinear term, a signal compression method is useful to identify a system because the equivalent impulse response of linear part from the system can be extracted by the method. However even though the signal compression method is useful to estimate uncertain parameters of the system, the method cannot be directly applied to a unique system with hysteresis characteristics because it cannot estimate all of the two different dynamic properties according to its motion direction. This paper proposes a signal compression method with a pre-processor to identify a unique system with two different dynamics according to its motion direction. The pre-processor plays a role of separating expansion and retraction properties from the system with hysteresis characteristics. For evaluating performance of the proposed approach, a simulation to estimate the assumed unknown parameters for an arbitrary known model is carried out. A motion platform with several single-rod cylinders is a representative unique system with two different dynamics, because each single-rod cylinder has expansion and retraction dynamic properties according to its motion direction. The nominal constant parameters of the motion platform are experimentally identified by using the proposed method. As its application, the identified parameters are applied to a design of a sliding mode controller for the simulator.

Fuzzy-sliding mode control of a polishing robot based on genetic algorithm

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaike’s information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

Implementation of a Robust Dynamic Control for SCARA Robot

A control system for SCARA robot is designed for implementing a robust dynamic control algorithm. This study focuses on the use of DSPs in the design of joint controllers and interfaces in between the host controller and four joint controllers and in between the joint controllers and four servo drives. The mechanical body of SCARA robot and the servo drives, are selected from the commercially available products. The four joint controllers, assigned to each joint separately, are combined into a common system through the mother board hardwarewise and through the global memory softwarewise. The mother board is designed to connect joint controllers onto the board through the slots adopting PC/104 bus structures. The global memory stores the common data which can be shared by joint controllers and used by the host computer directly, and it virtually combines the whole system into one. To demonstrate the performance and efficiency of the system, a robust inverse dynamic algorithm is proposed and implemented for a faster and more precise control. The robust inverse dynamic algorithm is basically derived from an inverse dynamic algorithm and a PID compensator. Based upon the derived dynamic equations of SCARA robot, the inverse dynamic algorithm is initially implemented with l msec of control cycle—0.3 msec is actually used for the control algorithm—in this system. The algorithm is found to be inadequate for the high speed and precision tasks due to inherent modelling errors and time-varying factors. Therefore a variable PID algorithm is combined with the inverse dynamic algorithm to reinforce robustness of control. Experimental data using the proposed algorithm are presented and compared with the results obtained from the PID and the inverse dynamic algorithms.

REAL-TIME PATH PLANNING IN UNKNOWN ENVIRONMENTS USING A VIRTUAL HILL

Abstract The artificial potential field based path planning has been most wisely used for local path planning because it provides simple and efficient motion planners for practical purposes. However, this approach has a local minimum problem which can trap a robot before reaching its goal. The local minimum problem is sometimes inevitable when a mobile robot moves in unknown environments, because the robot cannot predict local minima before it detects obstacles forming the local minima. The avoidance of local minima has been an active research topic in the potential field based path planning. In this study, we propose a new concept using a virtual hill to escape local minima that occur in local path planning for a mobile robot. A virtual hill is located around local minimum to repel a robot from local minimum.