A neural network–based sliding mode controller of folding-boom aerial work platform

Introduction

The folding-boom aerial work platform is a type of engineering vehicle which is used for enhancing the personnel to the designated place for installation and maintenance, as shown in Figure 1.¹ Therefore, it requires high stability for the security of people working on the platform. As the extensive use of light-long beam in the structure of arm system of aerial work platform, elastic deformation of beam cannot be ignored to guarantee work platform’s stable motion. For realizing trajectory tracking of aerial work platform, adaptive neural network controller is adopted in Jia et al.² and self-tuning fuzzy proportional–integral–derivative (PID) control scheme is proposed in Miao et al.³ However, the deformation of beam is not considered in the established model. Based on the theory of flexible multi-body dynamics and Lagrange’s equation, the model of folding-boom aerial work platform with flexible beam driven by hydraulic cylinder is established, and the vibration existed in flexible beam is shown in Hu et al.¹ Besides, the similar model is obtained, and fuzzy PID is used for the trajectory tracking of work platform in Meng,⁴ but this study only gives simulation results, and the stability of system is not proved. In addition, backstepping control method of work platform is proposed in Hu et al.⁵ based on the model in Hu et al.¹ However, this method can only be used for the accurate models. In fact, there exists parameter uncertainty in the model of folding-boom aerial work platform. Sliding mode control (SMC) has robustness to model uncertainty,⁶ which has been widely used for the control of nonlinear system. This article proposes an SMC method used for tracking control of aerial work platform. As a robust control scheme, SMC can make the state of the system move along the designed sliding mode surface using the switching control strategy. However, as the discontinuous switching property of SMC, it is difficult to make system state converge to equilibrium point along the sliding mode surface strictly. As a result, the chattering will be occurred. To reduce the chattering, several approaches have been proposed,^7–15 among which, neural network system is an alternative method.^13–15 Artificial neural network is derived from biological networks,¹⁶ which is a kind...