Iterative learning control with complex conjugate gradient optimization algorithm for multiaxial road durability test rig

Abstract

Service load replication performed on multiaxial hydraulic test rigs has been widely applied in automotive engineering for durability testing in laboratory. The frequency-domain off-line iterative learning control is used to generate the desired drive file, i.e. the input signals which drive the actuators of the test rig. During the iterations an experimentally identified linear frequency-domain system model is used. As the durability test rig and the specimen under test have a strong nonlinear behavior, a large number of iterations are needed to generate the drive file. This process will cause premature deterioration to the specimen unavoidably. In order to accelerate drive file construction, a method embedding complex conjugate gradient algorithm into the conventional off-line iterative learning control is proposed to reproduce the loading conditions. The basic principle and monotone convergence of the method is presented. The drive signal is updated according to the complex conjugate gradient and the optimal learning gain. An optimal learning gain can be obtained by an estimate loop. Finally, simulations are carried out based on the identified parameter model of a real spindle-coupled multiaxial test rig. With real-life spindle forces from the wheel force transducer in the proving ground test to be replicated, the simulation results indicate that the proposed conventional off-line iterative learning control with complex conjugate gradient algorithm allows generation of drive file more rapidly and precisely compared with the state-of-the-art off-line iterative learning control. Few have been done about the proposed method before. The new method is not limited to the durability testing and can be extended to other systems where repetitive tracking task is required.

Neuro-fuzzy iterative learning control for 4-poster test rig

“…For example, in De Cuyper (2006), researchers compare their results with linear ILC including inverse model controller, which is represented as a frequency response function. In another example given in Wang et al (2019a), the inverse model controller used in ILC is applied as a discrete-time transfer function. Similarly, with the second example, in this paper inverse model controller of linear ILC is represented as a transfer function.…”

“…In these studies, the weighting coefficient is updated with an adaptation rule, which is a function of the error ratio of previous trials. To update ILC gain, numerical optimization methods are used, such as Newton Method in Lin and Owens (2007), Quasi-Newton Method in Wang et al (2019b), complex conjugate direction method in Wang et al (2019a). The main advantage of the optimization-based ILC methods is that these methods can be easily applied to conventional inverse model-based ILC algorithms and satisfy monotonic convergence.…”

Decoupling Control of a Multiaxis Hydraulic Servo Shaking Table Based on Dynamic Model

“…As an important method, the conventional proportionalintegral-derivative controller cannot cover the control requirements of the increasingly complex hydraulic servo system [19]. us, a variety of modern control methods have been developed to improve the control performance of multiaxis, especially 6-DOF hydraulic servo shaking table [20]. Shen et al proposed inverse transfer function of the system based on three-variable controller and internal model control to improve the control accuracy of electrohydraulic system [21].…”

Further results on advanced robust iterative learning control and modeling of robotic systems

“…Namely, learning is a characteristic of human beings and ILC emulates human learning using the knowledge obtained from the previous trial in order to adjust the control input for the current trial and improve tracking output performance of the systems repetitively over a finite time interval. [24][25][26][27] A typical ILC in the time domain presents a simple off-line feedforward learning control and has failed to suppress the non-repeating disturbances. To improve performance, ILC is usually combined with feedback control.…”