If the product supply shortage occurs during the sales period, customers will turn to other companies, so the enterprise lose the sales opportunity. If the enterprise can predict product demands, and manages the product sales by using the supply chain management prediction system to improve the bottleneck of the inventory, the hot sales product can have more critical time utilization, and the inventory status can be reflected quickly by Internet of Things (IoT). To overcome the problem that the replenishment model cannot show the actual quantity of products on the store shelves, in the paper, we propose an intelligent agent-based prediction system, which serves as a framework to construct an integrated prediction system through the use of radio frequency identification (RFID) technology to design the intelligent product prediction shelf to extract product messages, and the service oriented architecture to develop prediction information to recommend products to the customer. The result of the paper proposes an agent-based cloud computing service platform in IoT and intelligent agents with SOA as backend cloud services. To build a prototype prediction system with performance analysis, it can be proved that the prediction system architecture for intelligent agent-based prediction system could improve operation performance and effectively enhance customer service quality for hot sale products.

A novel cascade visual control scheme is proposed to tailor for electrically driven robotic manipulators that operate under kinematic and dynamic uncertainties, utilizing an uncalibrated stationary camera. The proposed control approach incorporates adaptive weight radial basis function neural networks (RBFNNs) to learn the behaviors of the uncertain dynamics of the robot and the joint actuators. The controllers are designed to nullify the approximation error and mitigate unknown disturbances through an integrated robust adaptive mechanism. A major advantage of the proposed approach is that prior knowledge of the dynamics of the robotic manipulator and its actuator is no longer required. The controller autonomously assimilates the robot and actuator dynamics online, thereby obviating the need for fussy regression matrix derivation and advance dynamic measurement to establish the adaptive dynamic parameter update algorithm. The proposed scheme ensures closed-loop system stability, bounded system states, and the convergence of tracking errors to zero. Simulation results, employing a PUMA manipulator as a testbed, substantiate the viability of the proposed control policy.

The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiological tremor has been an important yet challenging research topic in the tele-operation robot field. Some scholars propose using deep learning algorithms to solve this problem, but a large number of hyperparameters lead to a slow training speed. Later, the support-vector-machine-based methods have been applied to solve the problem, thereby effectively canceling tremors. However, these methods may lose the prediction accuracy, because learning energy cannot be accurately assigned. Therefore, in this paper, we propose a broad-learning-system-based tremor filter, which integrates a series of incremental learning algorithms to achieve fast remodeling and reach the desired performance. Note that the broad-learning-system-based filter has a fast learning rate while ensuring the accuracy due to its simple and novel network structure. Unlike other algorithms, it uses incremental learning algorithms to constantly update network parameters during training, and it stops learning when the error converges to zero. By focusing on the control performance of the slave robot, a sliding mode control approach has been used to improve the performance of closed-loop systems. In simulation experiments, the results demonstrated the feasibility of our proposed method.

In the study, we propose a novel adaptive visual servoing control scheme for robotic manipulators with kinematic and dynamic uncertainties, where the camera used is uncalibrated, which implies that its intrinsic and extrinsic parameters are unavailable for measurement. For our scheme, a depth-independent composite Jacobian matrix is constructed to make visual parameters and robotic physical parameters appear linearly in a parametrized uniform form so that an adaptive algorithm can be developed to estimate their values. With the raised adaptive algorithm, the potential singularity of the Jacobian matrix can be well circumvented by updating estimated parameters in an appropriate tiny range of actual values. With our scheme, the asymptotic convergence of the image tracking error to zero is established successfully, in addition to the signal boundedness of the closed-loop system. The effectiveness of the proposed scheme is confirmed by simulation results based on a 6-DOF PUMA manipulator.

In this paper, we investigated the optimal tracking control problem of flexible-joint robotic manipulators in order to achieve trajectory tracking, and at the same time reduced the energy consumption of the feedback controller. Technically, optimization strategies were well-integrated into backstepping recursive design so that a series of optimized controllers for each subsystem could be constructed to improve the closed-loop system performance, and, additionally, a reinforcement learning method strategy based on neural network actor-critic architecture was adopted to approximate unknown terms in control design, making that the Hamilton-Jacobi-Bellman equation solvable in the sense of optimal control. With our scheme, the closed-loop stability, the convergence of output tracking error can be proved rigorously. Besides theoretical analysis, the effectiveness of our scheme was also illustrated by simulation results.