## 1 Introduction  
Narrow, confined agricultural facilities such as greenhouses, orchards, and warehouses have difficult vehicle requirements. Vehicles used in these facilities must be flexible and protect the environment [1; 2]. Currently, most transportation equipment used in these facilities is within the category of small conventional vehicles such as tractors, tricydes, mobile platforms, etc. Most of these vehicles are equipped with traditional powertrain systems. Inflexibility and exhaust emissions are two main problems with this equipment [3; 4]. It is critical to develop flexible, environmentally friendly, energy-sustainable vehicles for agricultural facility environments [5; 6; 7]. Electric vehicles (EVs) with four in-wheel motors have attracted tremendous attention in recent years because of their actuation flexibility [8; 9]. By taking advantage of distributed driving modes and steer-by-wire systems, each wheel can operate to its maximum capacity and the vehicle structure can be simplified radically. In addition, the electrification of agricultural machinery is essential to promoting energy independence and efficiency, and will eventually aid in cost reduction [10]. Therefore, this vehicle technology has the potential to solve the previously mentioned problems in agriculture. However, several challenges must be overcome for this type of vehicle to be made available. These challenges involve over-actuation, high control system complexity, and severe coordinated control requirements [11; 12; 13]. Thus, this study focuses on the motion control problem of a flexible electric vehicle, referred to as a flexible chassis (FC). The FC is a chassis with four independently driven in-wheel motors that can flexibly perform multiple types of motion, making it suitable for confined facility environments. Numerous valuable studies of EV control methodology were recently conducted in agricultural facility machinery areas. These studies mainly involved objects such as electric chassis, mobile robots, and electric platforms. [PERSON] et al. developed a steering control algorithm based on an overhead guide to improve the stability of an autonomous greenhouse harvesting and spraying vehicle [14]. Based on sliding mode control, [PERSON] et al. proposed a robust controller for a four-wheel drive and steering agricultural vehicle. The researchers demonstrated good controller capabilities and robustness in controlling a system with a high degree of freedom [15]. [PERSON] et al. designed a fuzzy logic controller to adjust drive wheel speed and achieved teleoperated steering for agricultural vehicles [16]. [PERSON] and [PERSON] designed a general-purpose electric chassis for agricultural tasks such as mapping, detection, guidance, and action based on human-centered design frameworks and processes [17]. Various researchers have focused on mobile robots due to agricultural facility equipment intelligence requirements. [PERSON] et al. proposed a novel extended Ackerman steering principle for an agricultural mobile robot. Compared with the conventional Ackerman steering principle, the proposed strategy can reduce the energy consumption of the entire machine [18]. [PERSON] et al. introduced a proportional integral derivative (PID) algorithm into a laser navigation control system. Their study achieved low-speed steering control of a greenhouse tomato-harvesting robot based on the Ackerman steering principle [19]. [PERSON] and [PERSON] proposed a two-stage guidance control scheme for a low-cost planting robot. This system could regulate the direction of the robot and minimize the lateral error associated with navigation [20]. Classical control methods such as PID control methodology, the frequency response, and root locus methods have been commonly used to improve overall EV performance. However, these methods require complex, precise modeling [21; 22; 23; 24; 25] and are incapable of tackling the nonlinear problem of EVs [26].