Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Studyon hardware-in-the-loopsimulation of beer fermentation system

Beer fermentation process is a complex biochemical reaction process.It is the most important to control temperature of the wort in fermentation tank in accordance with the beer fermentation temperature curve so as to ensure the completion of fermentation.The controlled object is characterized by large inertia,long time delay and mutual coupling of three temperature areas.Based on this,a temperature control method for beer fermentation system is designed.Using digital incremental proportion integration differentiation （PID） control algorithm,the controlled quantity is transmitted to the controlled object after diagonal matrix decoupling.This simulation system can be completed in laboratory using VB and Kingview software,so it has the features of good security and low cost.It is very suitable for experimental teaching.

Structure Analysis and Control Optimization on High Speed Lift Platform

Dynamic analysis of scissor hydraulic lift platform has been performed to invest/gate the key factors which determine size and shape of the platfolan. By using MATLAB, the position of hydraulic cylinder has been optimized to reduce jacking force of piston and the whole system. Thus structure deformation decreases which is beneficial to control accuracy. Additionally, a new proportion integration differentiation （PID） control mode based on BP neural network has been developed to improve the stability and accuracy for the pasitio^L control in this system. Compared with existing PID tuning meth~~ls and fuzzy self-adjusted PID controllers, the proposed back propagation （BP） based PID controller can achieve better performance for a wide range of complex processes and realize self-tuning of parameters. It was confirmed that the performance of the lift platform regarding the force variation and position accuracy was greatly enhanced by optimizing of the system both in structure and control. Considerable economic benefit can also be achieved thrangh the application of this intelligent PID system.

Application of linear active disturbance rejection control for photoelectric tracking system

Dynamic characteristics and tracking precision are studied in the photoelectric tracking system and a linear active disturbance rejection control( LADRC) scheme is proposed for position loop. A current and speed controller is designed by a transfer function model,which is obtained by adaptive differential evolution. Model error,friction and nonlinear factor existing in position loop are treated as ‘disturbance',which is estimated and compensated by generalized proportional integral( GPI)observer. Comparative results are provided to demonstrate the remarkable performance of the proposed method. It turns out that the proposed scheme is successful and has superior features,such as quick dynamic response,low overshoot and high tracking precision. Furthermore,with the proposed method,friction is suppressed effectively.

Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains

The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation （PID） controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization （MCIWO） and particle swarm optimization （PSO） algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.