# A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics
## 1 Introduction
The authors of [16] proposed beetle antenna searching (BAS) and particle swarm optimization (PSO) algorithms to determine the appropriate PID parameter settings to control a dual-wheel AGV robot. In the same vein, an intelligent approach based on an optimization technique was proposed in [17]. The butterfly optimization algorithm (BOA) was used to set the optimal parameters of a PID controller. A coupled strategy including an EMRAN architecture with an extended Kalman filter, PID cruise, and a Stanley controller was suggested in [18]. This EMRAN-based method's performance concerning RMS tracking errors has shown its superiority over the Stanley controller, fuzzy-based PID, and conventional PID. On top of providing great robustness, the EMRAN-assisted controller has demonstrated its ability to cope in extreme situations. This process focused on the improvement of ADASs' ACC feature. An intelligent robust adaptive exponential-like sliding mode was offered by [19]. A type-2 fuzzy neural network (T2 FNN) was proposed to estimate the unidentified vehicle dynamics. The T2 FNN-based hierarchical controller aiming at the closed-loop stability of the system has guaranteed the convergence of the heading error to zero. Intelligent high-speed trains incorporating backstepping control and deep learning were investigated in [20]. A long short-term memory network (LSTM) merged with a fully connected neural network has been utilized. The approach has presented an excellent follow-up of the high-speed profile while providing a suitable steady-state separation within the safety band. This technique aims at improving the ACC feature. To innovate within the scope of the same function, a hybrid structure combining the PID, MPC, and adaptive-network fuzzy inference system (ANFIS) was provided in [21]. This technique exhibited a good indicator in terms of acceleration comfort due to being less aggressive than the other controllers involved in this study; moreover, its tracking error reached 95% of the reference. Beyond that, the ANFIS controller showed an average error of 0.07 m/s\({}^{2}\) when the acceleration was at its maximum. The authors of [22] proposed a multi-objective model predictive control (MPC) design targeting improving a vehicle's active suspension control. The proposed approach has shown promising results through experimental tests related to the suspension system on a quarter car in laboratory conditions. A combination of MPC, feedforward, and PID feedback was discussed in [23]. MPC was designed to determine longitudinal acceleration under the driver's expectations. The built-in PID feedback and anticipation were adopted to ensure the desired longitudinal acceleration tracking by acting on the longitudinal actuators; this approach aimed to contribute to the ACC feature of ADASs. Along the same lines, [24] discussed a self-organizing neuro-fuzzy controller. This intelligent technique incorporated a neural network and a fuzzy system to automatically adapt the vehicle's speed by learning from the human pilot and utilizing anticipation. This process targeted the innovation at the level of the ISA feature of the ADAS. The authors of [25] sought to address the contribution to the autonomous emergency braking (AEB) feature through the proposed approach of a PID and a Kalman filter. This strategy aimed to determine adequate speed profiles to prevent collisions. For low-friction roads, a single neural PID combined with a Kalman filter was offered to find the maximum deceleration, whereas for high-friction roads the desired deceleration was achieved according to comfort and safety. This approach was presented as a non-traditional AEB model. The authors of [26] discussed a radial basis functions neural network (RBNN)-based SMC control structure that contributed to both ACC and ISA. In this study, the upper limit of disturbance was estimated with the RBNN. On this basis, the SMC was deployed to design a longitudinal velocity controller.