In this paper, we propose two methods of adaptive actor-critic architectures to solve control problems of nonlinear systems. One method uses two actual states at time k and time k+1 to update the learning algorithm. The basic idea of this method is that the agent can directly take some knowledge from the environment to improve its knowledge. The other method only uses the state at time k to update the algorithm. This method is called, learning from prediction (or simulated experience). Both methods include one or two predictive models, which are assumed to be applied to construct predictive states and a model-based actor (MBA). Here, the MBA as an actor can be viewed as a network where the connection weights are the elements of the feedback gain matrix. In the critic part, two value-functions are realized as a pure static mapping, which can be reduced to a nonlinear current estimator by using the radial basis function neural networks (RBFNNs). Simulation results obtained for a dynamical model of nonholonomic mobile robots with two independent driving wheels are presented. They show the effectiveness of the proposed approaches for the trajectory tracking control problem.
Abstract-To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse
Abstract.A control strategy is proposed here for four-rotor vertical take-off and landing (VTOL) aerial robot called X4-flyer. Since the X4-flyer has underactuated and nonholonomic features, a kinematics control law is first derived using Astolfi's discontinuous control. A backstepping method that is one of adaptive control methods based on Lyapunov methods, then provides the kinematic based inputs, to construct the torque control of X4-flyer. Finally, computer simulations are given to demonstrate the effectiveness of our approach.
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