## 1 Introduction  
Dynamic load identification is a relatively wide area of research that covers a range of applications [1; 2; 3; 4; 5]. It is crucial to evaluate the dynamic loads on a mechanical system in many applications such as design optimization, diagnostics, control and monitoring. It is common to record the system response in terms of displacement, velocity or acceleration which can then be processed offline to evaluate the external forces/loads. Although this is a relatively well-established area of research but it is still under continuous development where several challenges remains to be unsolved [2; 6; 7]. A major challenge in this regards is about moving the load identification process from offline to online computations so that the dynamic load is evaluated in real time and while the system response is still being measured. In this paper, we focus on identifying the dynamic load in real time which we refer to as the online load identification. This challenge is mainly motivated by the automotive industry and is highly relevant for applications in autonomous vehicles [8; 9; 10]. For example in autonomous vehicles it is important to evaluate the external dynamic load on a vehicle to take decisions related to acceleration/deceleration or trajectory optimization [9]. Having real-time accurate estimates of the dynamic loads on such systems can help to prevent catastrophic failures. In recent decades the dynamic load identification technology has seen consistent improvements where several algorithms were developed including wavelet transform [11; 12], neural network-based [13; 14] and inverse system [15; 16] methods. Generally speaking the developed algorithms can be organized into two categories [17; 18; 19], namely time domain [20; 21] and frequency domain [22; 23] methods. Each category presents several advantages and disadvantages. The frequency domain methods are in general simple to implement but they are not suitable for analyzing transient responses [24; 25; 26; 27]. This limits the applicability of these methods to dynamic systems that involve a transient load. On the other hand the time domain methods can work with non-stationary loads but unless very small time steps are used such methods can accumulate large errors over the time [20; 28]. The time domain-based methods combine the equation of motion with a discrete time history. In this paper we implement the second inverse problem to recover the dynamic load on a mechanical system in the time domain [29]. This class of inverse problems reconstruct the dynamic loads using the response data and the structural system specifications. The solution of the second inverse problem has been a general base for several developments in dynamic load identification. A single-point method was used to identify loads distribution in power systems for both single-phase and three-phase applications [30]. Using accelerations measured in a Bohai offshore platform it was possible to identify the dynamic ice load [31]. Different approaches were proposed to deal with uncertainties in the problem [7; 8]. However, the main body of the published literature focuses mainly on the offline identification approach i.e., reconstructing the dynamic load history offline after collecting the response information online. This situation is mainly driven by the intensive computations that the inverse solution requires and at the same time the limited computing resources that can be dedicated to a structural system in order to process the response. It is also related to the high frequency rate at which the dynamic loads could be changing such as in vibrating structures. Therefore, limited amount of literature is available on real-time dynamic load identification where the response is processed online [32; 33]. Although time domain methods are successfully used to identify the dynamic load offline but they need to be further developed before they can be adopted for online load identification.