István Kecskés

Optimization of PI and Fuzzy-PI Controllers on Simulation Model of Szabad(ka)-II Walking Robot

The Szabad(ka)-II 18 DOF walking robot and its simulation model is suitable for research into hexapod walking algorithm and motion control. The complete dynamic model has already been built, and is used as a black box for walking optimization in this research. First, optimal straight line walking was chosen as our objective, since the robot mainly moves in this mode. This case can be tested and validated as well on the current version of our robot. An ellipse-based leg trajectory has been generated for this low-cost straight line walking. Currently a simple new Fuzzy-PI controller with three input variables is being constructed and compared with an previously used PI controller. The purpose of defining the rules and its optimization are to obtain a controller that provides walking with higher quality. Both the compared controllers have been optimized together with the parameters of the leg trajectory. The particle swarm optimization (PSO) method was chosen from several methods with our benchmark-based selection research and the help of specific test functions; moreover the previous research (the comparison of genetic algorithm (GA) and PSO) also led to this conclusion.

Protective Fuzzy Control of Hexapod Walking Robot Driver in Case of Walking and Dropping

The new hexapod walking robot is assembly phase. It was design to overcome rough terrain using the latest numerical tests on the model. One of the likelihoods of walking on rough terrain is falling over. This posed the requirement that the robot had to be able to continue walking even after multiple falls. One of the goals was to create a control mechanism in the engine layer that will ensure optimal walk as well as protection from breaking down. In order to achieve the best results, it would be necessary to ensure the feedback of the torque, but there was no possibility for that. Thus the only solution was to feedback the engine power; however, such feedback will input a delay into the feedback branch anyway. The suitable Fuzzy rules are being sought for, which will help minimize the effects of the delay in the control branch. The fitness function is defined for determining the optimal hexapod walk algorithm. During the test the worst cases were tested with the lower arm of the robot closing in a 90 degree angle with the upper arm due to the fall, this is when the robot structure is endures maximum load.

Optimization of the hexapod robot walking by genetic algorithm

In building walking hexapod robot great time and effort is needed to optimize robot walking. When simulating robotic gaits, several parameters affect simulation output. These parameters need to be optimized in order to achieve optimal robot movement. Genetic algorithm is used to optimize parameters in the simulation.

Modeling and Fuzzy control of a four-wheeled mobile robot

While studying the movement of driven robots a four-wheeled car is one of the most commonly used and preferred models. In the modeling process it can be assumed that the motion occurs in a two-dimensional space and the object can be driven forward or backward. In the foreseen task, the robot must reach a target point by some predefined specific control requirements. The simplified kinematic bicycle model of a four-wheeled robot car, mentioned in [1] has been upgraded for the purposes of this research. It has been observed that the simplified model was not sufficiently adequate and accurate. Reference [1], besides the previous simplified model, also contains a P route controller. A new Fuzzy route control has been applied, because it was more customizable compared to the simple PID control. This article describes a comparison of the results between P and Fuzzy controllers. Based on the results obtained it has been concluded that it would be worthwhile to further develop this model and control system.

Fuzzy route control of dynamic model of four-wheeled mobile robot

The dynamic model calculates the forces and electric activities that appear during the movement of the robot. The article [1] demonstrates a full kinematic model of a four-wheeled robot car which we have expanded with a minimal dynamic model in this work. Fuzzy route control was applied, because it was more customizable than the simple PID control. In the foreseen task, the robot must reach a target point by some predefined specific requirements. We performed a comparison between the P route controller solution in article [2] and our Fuzzy controller for a case. Fuzzy logic, one of the most suitable soft computing methods which can represent the knowledge base of the routing requirement [3]. We developed fitness function evaluation of driving, for which the kinematic and dynamic characteristics are required.

Full kinematic and dynamic modeling of “Szabad(ka)-Duna” hexapod

This article describes the creation MATLAB model for the six-legged walking robot by the name of new “Szabad(ka)-Duna”, starting from the creation of the motor model through the inverse kinematic and inverse dynamic model to the insertion of the control algorithms.

Fuzzy control of a two-wheeled mobile pendulum system

In this paper, the fuzzy control of a mechatronic system will be studied. The mechatronic system is a special mobile robot (called two-wheeled mobile pendulum) that has two contact points with the supporting surface and its center of mass is located under the wheel axis. Due to the mechanical structure, the inner body (which acts as a pendulum between the wheels) tends to oscillate during the translational motion of the robot, thus the application of complex control solutions is essential in order to stabilize the dynamical system. In the first part of the paper the mechatronic system and the corresponding mathematical model are introduced, while in the second part the design and implementation of different fuzzy controllers are elaborated for the plant. The achieved control performances are analyzed based on simulation and implementation results.

Swarm-based optimizations in hexapod robot walking

During previous research [1-7] and development several hexapod walking robots and its simulation model were built by the authors. The latest model called Szabad(ka)-II is a complex, servo motor driven, multiprocessor device. In parallel with the building of this hexapod robot, a simulation model was also built in order to help optimize the robots structure, walking and driving [5]. The results of modeling and parameter optimizations can be used as a guideline during the design of a new and improved robot. The Particle Swarm Optimization (PSO) method was chosen because its simplicity and effectiveness [1, 2]. It has produced better and faster results compared to previously used Genetic Algorithm (GA) [3]. However, neither selected method is able to provide the global optimum in the case of one-time run. Using an optimization benchmark disclose the differences and help to get the best parameterized optimization method for a given problem.

Multi-scenario optimization approach for fuzzy control of a robot-car model

A simple dynamic model of a robot-car has been built in the Matlab/Simulink environment [1], which expanded with a minimal dynamic part [2]. A fuzzy route controller was developed and its performance compared to the PID control [2]. The multi-scenario simulation with five different spatial target points is using in order to represent the all expected scenarios during the controller optimization. The multi-objective fitness evaluation of the driving has also been developed based on kinematic and dynamic characteristics. The optimization of the Fuzzy route controller is performed on the multi-scenario simulation using previously implemented heuristic optimization methods [3]. The multi-scenario optimum is compared with the single-scenario optimums, and evaluated in that way.

Embedding optimized trajectory and motor controller into the Szabad(ka)-II hexapod robot

The driving of the Szabad(ka)-II hexapod robot was characterized with its minimal and non-optimized structure during the development of the robot. For this driving algorithm, a simulation environment has been developed, and based on the simulation model and recorded measurement results, the aforementioned algorithm has been validated. In the next step of the development, the optimization of the robot driving algorithm was elaborated in its simulation environment. In order, to implement the optimized controllers and trajectory, the software of the embedded system has to be rewritten, through which the developed fuzzy controllers and the high resolution leg-trajectories could be run on the robot. This paper on the one hand, introduces the essential aspects of the implementation and realization procedures of the optimized driving algorithm, on the other hand, gives a comparison assessment of the old and the new driving algorithms.