ABSTRACT
Accuracy and robust trajectory tracking for electro-hydraulic servo systems in the presence of load disturbances and model uncertainties are of great importance in many fields. In this work, a new adaptive sliding mode control method based on the RBF neural networks (SMC-RBF) is proposed to improve the performances of a robotic excavator. Model uncertainties and load disturbances of the electro-hydraulic servo system are approximated and compensated using the RBF neural networks. Adaptive mechanisms are designed to adjust the connection weights of the RBF neural networks in real time to guarantee the stability. A nonlinear term is introduced into the sliding mode to design an adaptive terminal sliding mode control structure to improve dynamic performances and the convergence speed. Moreover, a sliding mode chattering reduction method is proposed to suppress the chattering phenomenon. Three types of step, ramp and sine signals are used as the simulation reference trajectories to compare different controllers on a co-simulation platform. Experiments with leveling and triangle conditions are presented on a robotic excavator. Results show that the proposed SMC-RBF controller is superior to existing proportional integral derivative (PID) and sliding mode controller (SMC) in terms of tracking accuracy and disturbance rejection.
ABSTRACT
In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional-integral-derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler-Nichols (Z-N)PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller.
