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A new autonomous navigation control system is presented for mobile robots based on the affective cognitive learning and decision making (ACLDM) model. The behaviors of robot navigation are designed by dynamic system approach, which has a sound theoretical foundation for the system stability analysis. Cognitive states for work environment of the mobile robot(More)
This paper presents a new approach of structure identification and parameter estimation for Hammerstein Model by using particle swarm optimization (PSO). The average square error criterion (ASE) has been proposed to decrease computation and obtain the true optimal structure effectively. Meanwhile, the modified identification algorithm is always converging(More)
A novel control scheme with an adaptive neurodynamics and sliding mode control for the dynamic tracking control of a noholonomic mobile robot is presented. A biologically inspired neural model is embedded into the standard backstepping-based velocity controller to eliminate or inhibit the sharp speed jumps of velocity commonly existing in mobile robots due(More)
A new autonomous navigation control framework is presented for mobile robots by integrating affective cognitive learning and decision making (ACLDM) model with behavior-based robot system. Cognitive states for work environment of mobile robot are gotten from a pattern classifier based on Adaptive Resonance Theory-2 (ART-2) network. Then rational strategies(More)
A control scheme for dynamic tracking of mobile robots is presented, which integrates a velocity controller based on backstepping techniques and a torque controller based on improved RBF neural networks. The proposed torque control strategy derived from sliding modes depends on the dynamics of mobile robots. Because of the uncertainties in robot dynamics,(More)
A novel hybrid control strategy is developed for real-time motion tracking control of a nonholonomic mobile robot. The biological neurons, called as shunting models, are embedded into the backstepping-based velocity planner to eliminate the sharp speed jumps of velocity commonly existing in mobile robots due to tracking errors changing suddenly. The outputs(More)
Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and decision making. Skyline consists of all points that are not dominated by, or not worse than other points. It is a candidate set of optimal solution, which depends on a specific(More)
A novel tracking control scheme for nonholonomic mobile robots is developed, which integrates two adaptive neurons or non-adaptive neurons, backstepping technique and sliding mode. The proposed control system includes a velocity controller embedded with two biological neurons and a torque controller based on sliding mode. An adaptive neuron model is(More)
A hybrid navigation approach for mobile robots is presented in this paper, which integrates partial motion planning with emotion-based behavior coordination. According to the local sensing information and assigned goal, the partial motion planning determines a local target point for the mobile robot, and the trajectory composed of the local targets is near(More)
A novel stable tracking control scheme with embedded biological neurons is developed for a class of mobile robots. Two individual biological neurons are embedded into the backstepping-based velocity controller to eliminate the sharp jumps of linear and angular velocities as the position tracking errors change suddenly. This control system includes a(More)