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For complex working environments, traditional path planning algorithms for mobile robots are inefficient and difficult to get the optimal path. To better solve this problem, this paper introduces the fuzzy model for modeling and analysis. On the basis of elaborating the logical relationship between the T-S fuzzy model and the Kripke structure, the basic model of the robot’s moving path is designed and the map representation of the moving path is constructed. Based on the minimal repair algorithm of the fuzzy model, the virtual path fuzzy rule of the robot movement is established, and the hierarchical fuzzy control system is constructed by combining the robot kinematics movement model, and simulation experiments are carried out to verify the effectiveness of the above method. The state response x1, x2 of the hierarchical fuzzy control system realizes zero convergence at 5.12s and 3.91s, respectively, and the zero convergence time of the fuzzy control input is 79.23% lower than that of the Takagi-Sugeno fuzzy system. The lateral error of the hierarchical fuzzy control robot movement is approximately 0.05m, and the path length decreases from 1.38% to 4.37% with the map scale increasing. The use of a fuzzy model minimal repair algorithm can improve the efficiency of robot movement and obtain a relatively optimal path in a shorter time.

This paper proposes a variable structure control approach for vehicles platooning based on a hierarchical fuzzy logic. The leader-follower vehicle dynamics with model uncertainties is discussed from the viewpoint of a consensus problem. A practical two-layer fuzzy control for the platooning is designed by employing two common spacing policies to ensure system robustness in different scenarios. The two policies, i.e., constant distance and constant time headway, utilize the predecessor-successor information flow from the immediate predecessor and follower other than controlled vehicles. The first layer of the fuzzy system combines spacing control with velocity-acceleration control to achieve a rapid tracking for the desired control commands, and the second layer combines the sliding mode control to adaptively compensate for reducing the state errors caused by parameter uncertainties and disturbances. Shift between different controller parameters is based on performance boundaries to guarantee the stability of individual vehicle and platooning for arbitrary initial spacing and velocity errors. These performance boundaries can be determined by using a Lyapunov method with exponential stability. Simulation of a ten-vehicle large platooning with two spacing policies shows that the control performance of the newly proposed method is effective and promising. [ABSTRACT FROM AUTHOR]

In this paper, we illustrate a proposed method for control that combines the outputs of several individual controllers to improve global control of complex nonlinear plants. In the first part of this paper, we illustrate the proposed method that consists of two levels, where in the top level a fuzzy system represents a superior control that is designed for adjusting the behavior of the individual fuzzy controllers at the lower level. To test the approach, we consider the problem of flight control because it requires several individual controllers. Also a comparison is performed, where the hierarchical control strategy is compared with a simple control approach using the t student test. In this paper, we show that the proposed method outperforms the conventional fuzzy control approach. In the optimal design of the proposed control architecture a genetic algorithm was also applied to tune the parameters of the fuzzy systems in an optimal fashion. [ABSTRACT FROM AUTHOR]

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robot's mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot. [ABSTRACT FROM AUTHOR]

This paper presents a novel compositional method for finding fuzzy rules in a three-layered hierarchical fuzzy structure. The proposed method incorporates a multi-objective evolutionary algorithm and a large set of initial conditions, including dynamical conditions of the system under investigation. The proposed method is focused on handling the large set of initial conditions by a multi-objective evolutionary algorithm and it can be applied to a wide range of dynamical control systems in robotics. The method has been evaluated on a dynamical system such as the inverted pendulum. The experimental results and analysis showed that the proposed method is much better than the existing methods such as amalgamation and single objective evolutionary algorithm based methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper represents an artificial neural network (ANN) backpropagation algorithm is used to choose best coefficients of hierarchical fuzzy power system stabilizer (HFPSS). PSS is used for stability enhancement of a single machine infinite bus (SMIB) power system. ANN algorithm is used to predict load condition of the power system. And according to the predicted load condition ANN determinates choosing optimal parameters of the hierarchical fuzzy controller (HFC) to achieve better performance. Simulation results are compared with conventional PSS (CPSS) to show the effectiveness of the proposed controller. Also quantitative criterias of measuring performance is computed for 16 loading conditions. [Copyright &y& Elsevier]

Abstract: The research focuses on the issue of controlling a manipulator attached to a deployment, which typically exhibits compliance due to its mechanical nature. The problem is generalized as a motion control of a robotic manipulator attached to oscillatory bases. Due to the complexity of this problem, we decompose the control task into two subtasks and distribute them over two different levels. In the design of hierarchical fuzzy control structure, the lower level controllers take into account each subsystem ignoring the interactions among them, while the upper level controller handles subsystem interactions. The upper level coordinator to deal with the model reduction error and makes the supervisory decision to the lower level. Moreover, the supervisory fuzzy rule set is used to adjust the correction factors of the hierarchical fuzzy controller to achieve better performance. Consequently, it is shown that the proposed control model offers several implementation advantages such as less on-line computation time, reduced effect of overshoot and chattering, and a fast convergent rate in simulation. The results of this study can be feasible to various mechanical systems, such as mobile robot, gantry cranes, underwater robot, and other dynamic systems mounted on oscillatory bases. [Copyright &y& Elsevier]

This paper presents the design of a hierarchical neuro-fuzzy current control scheme for a shunt active power filter compared with a single fuzzy controller scheme. A single fuzzy controller scheme is presented first and an ANFIS based neuro-fuzzy controller is connected hierarchically to the first one to improve the performance. Simulation results show that harmonic compensation is achieved with both schemes, however switching performance is superior for hierarchical scheme. The method of switching controller development in this paper is new and can be applied to other power electronic converter applications for improved performance. [ABSTRACT FROM AUTHOR]

A new kind of mapping rule base scheme is proposed to get the fuzzy rules of hierarchical fuzzy systems. The algorithm of this scheme is developed such that one can easily design the involved fuzzy rules in the middle layers of the hierarchical structure. In contrast with the conventional single layer fuzzy controller, the present method has approximate performance using the same scaling factors. Next, examples are given. At last, simulated results demonstrate that the algorithm is effective and feasible. [Copyright &y& Elsevier]

A combined PD and hierarchical fuzzy control is proposed for the low-speed control of the C-axis of CNC turning centers considering the effects of transmission flexibility and complex nonlinear friction. Learning of the hierarchical structure and parameters of the suggested control strategy is carried out by using the genetic algorithms. The proposed algorithm consists of two phases: the first one is to search the best hierarchy, and the second to tune the consequent center values of the constituent fuzzy logic systems into the hierarchy. For the least total control rule number, the hierarchical fuzzy controller is chosen to include only the simple two-input/one-output fuzzy systems, and both binary and decimal genes are used for the selection, crossover and mutation of the genetic algorithm. The proposed approach is validated by the computer simulation. Each generation consists of 30 individuals: ten reproduced from its parent generation, ten generated by crossover, and the other ten by mutation. In the simulations, the C-axis is assumed to be driven by a vector-controlled AC induction motor, and the dynamic friction model suggested by Canudas de Wit et al. in 1995 is used. [ABSTRACT FROM AUTHOR]