In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.

Considering the non-rigid transmission connection between the wind turbine and the generator, their angular velocity is inconsistent. A DC-link voltage control model with uncertainties is established by a two-mass inertial model. The system uncertainties increase greatly compared with the single-mass inertial model, which aggravates a DC-link voltage accuracy. To suppress the uncertainties, Complementary sliding mode control (CSMC) is proposed to regulate the DC-link voltage since it has a strong robustness. The equivalent controller for the voltage loop is obtained by the conservation principle of AC and DC side power, and the compensation controller for the uncertainties is designed by the saturation function. The CSMC system is analyzed to be asymptotic stability. The simulation and experiment results show that CSMC system can suppress the system uncertainties, and obtain a more little steady-state error of the DC-link voltage by compared with PI control system.

This study investigates the trajectory tracking strategy of a snake robot with sideslip disturbance and unknown model parameters. To guide the robot to track the ideal trajectory faster and more accurately, an adaptive anti-sideslip strategy for a snake robot with the Integral Line-of-Sight (ILOS) function is reported. This technique eliminates direction sideslip and error fluctuation by using auxiliary integral terms and shortens the convergence time of state variables. Following the position and angle control objectives, the proposed controller considers the negative effects caused by the uncertainty and time variability of environmental parameters and compensates for the joint input using the adaptive update laws. The environment adaptability and tracking efficiency are improved. The stability analysis indicates that the state errors converge to the origin. The simulation and experiment data verifies the effectiveness and strength of the work. Note to Practitioners —This article was motivated by the problem of robust trajectory tracking for a snake robot in an environment with sideslip disturbance and unknown model parameters. In this environment, information of the motion space (for example, the coefficient of ground friction) cannot be obtained. In addition, there may be other system limitations (for example, motion sideslip limitations) and other operational limitations. These limitations are caused by the requirements of various common trajectory tracking objectives. These cases should also be considered in the control strategy. However, based on the existing methods of tracking control for snake robots, there is still a lack of a complete and reliable autonomous control scheme that can consider the above problems. On this basis, we present a reliable control strategy, which considers the above problems and the dynamic uncertainty of the model. In the future, we will extend the proposed method to the field of formation tracking control for multiple robots.

Since the sideslip angle is often ignored or simplified in the process of path following of unmanned surface vehicle by using the line-of-sight (LOS) guidance law because of its fast change and the difficulty of measurement, an observer was proposed by internal model control (IMC) to quickly estimate the sideslip angle in the LOS guidance law. First, a prediction model was established for the tracking error, and a state space model for prediction errors was constructed as an internal model. With the introduction of the auxiliary variables, a new augmented system was set up for a state space model of the prediction errors. Then, the sideslip angle observer was designed by the theory of state feedback with the feature of the control law of a proportional-integral type. Theoretically, the stability of the system was proved based on the Lyapunov criteria. A simulation and experiment verified the effectiveness of the proposed sideslip angle observer in improving the path-following accuracy. The results show that the IMC-based observer introduces a proportional term of tracking error that is not considered by other observers, which is easier to implement and adjust, and has a faster response speed and a smaller steady-state error for the sideslip angle. In addition, the assumption of a small sideslip angle is not introduced in the design process, so the proposed observer provides an accurate estimation method for a large sideslip angle.