In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for the multiple nonholonomic mobile robots formation. First, a model-based monocular vision method is developed to obtain the location information of the leader. Then, a separation-bearing-orientation scheme (SBOS) control strategy is proposed. During the formation motion, the leader robot is controlled to track the desired trajectory and the desired leader-follower relationship can be maintained through the SBOS method. Finally, the model predictive control (MPC) is utilized to maintain the desired leader-follower relationship. To solve the MPC generated constrained quadratic programming problem, the neural-dynamic optimization approach is used to search for the global optimal solution. Compared to other existing formation control approaches, the proposed solution is that the NMPC scheme exploit prime-dual neural network for online optimization. Finally, by using several actual mobile robots, the effectiveness of the proposed approach has been verified through the experimental studies.

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Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot to achieve cooperative manipulation with humans. The control scheme consists of two layers. In low-level control of the upper limb exoskeleton robot, an admittance control scheme with an asymmetric barrier Lyapunov function-based adaptive neural network controller is proposed to enable the robot to be back drivable. In order to achieve high-level interaction, a strategy for learning human skills from demonstration is proposed by utilizing Gaussian mixture models, which consists of the learning and reproduction phase. During the learning phase, the robot observes and learns how a demonstrator performs a specific impedance-based task successfully, and in the reproduction phase, the robot can provide the subjects with just enough assistance by extracting human skills from demonstrations to prevent the motion of the robot end-effector deviating far from desired ones, due to variation in the interaction force caused by environmental disturbances. Experimental results of two different tasks show that the proposed control scheme can provide human subjects with assistance as needed during cooperative manipulation.

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Due to the nature of involved optics, the depth of field in imaging systems is usually constricted in the field of view. As a result, we get the image with only parts of the scene in focus. To extend the depth of field, fusing the images at different focus levels is a promising approach. This paper proposes a novel multifocus image fusion approach based on clarity enhanced image segmentation and regional sparse representation. On the one hand, using clarity enhanced image that contains both intensity and clarity information, the proposed method decreases the risk of partitioning the in-focus and out-of-focus pixels in the same region. On the other hand, due to the regional selection of sparse coefficients, the proposed method strengthens its robustness to the distortions and misplacement usually resulting from pixel based coefficients selection. In short, the proposed method combines the merits of regional image fusion and sparse representation based image fusion. The experimental results demonstrate that the proposed method outperforms six recently proposed multifocus image fusion methods.

The fixed-time control for systems with timevarying parameters is investigated in this article. Different from the previous pure robust control, the proposed method is less conservative. Applying this method could avoid the existence of unknown time-varying parameter derivatives in control design. Combining with adaptive fuzzy control and projection operator, a novel singularity-avoidance virtual controller is constructed via backsteping. The proposed controller guarantees that the tracking errors converge to a user-defined precision within a prescribed fixed-time. Two examples are used to show that this method can achieve fixed time control even if the time-varying parameters are unknown and non-asymptotically constant.