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.

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.

In this paper, a kind of temperature sensor for fermentation bed in large scale chicken farm was studied. The temperature sensing equipment includes a temperature sensor housing, a circuit board and a temperature probe detection sensor. The circuit board is provided with a microprocessor and a wireless Bluetooth communication module. The temperature probe detecting sensor is fixedly connected with the sensor housing, and at least two temperature detecting elements are arranged inside the temperature probe detecting elements, which are spaced inside the probe tube. The microprocessor is connected with the temperature detecting element and wireless Bluetooth module respectively. The temperature sensor is used to detect the wall temperature of the probe tube and generate temperature signals. The microprocessor is used to receive the detection signals and forward them to the wireless Bluetooth module. The wireless Bluetooth module is used to convert the temperature detection signals into wireless signals and send them out.