## 1 Introduction
This method has the advantage of not generating additional inertia during the movement of the manipulator, and the testing system can achieve good gravity balance within the required accuracy range. Although the effect of active gravity compensation may be slightly inferior to that of passive gravity compensation in static gravity balance, it still has a very high static balance capability and is an effective method. In the design of an active gravity compensation space manipulator test bed, the precision and response speed of the control system can be improved through improved mechanism design and control algorithms to continuously improve the gravity compensation effect of the manipulator on the ground. The test system designed in this paper used active gravity compensation. In order to achieve good gravity compensation, we designed an intelligent controller and used ADP to adjust the parameters. Finally, the coefficients were fine-tuned based on actual performance. Adaptive dynamic programming (ADP) is a data-driven adaptive optimal control approach. Researchers have proposed novel event-triggered control approaches to solve the adaptive optimal output regulation problem for a class of linear discrete-time systems [8] and an event-triggered robust optimal control approach for large-scale systems with both parametric and dynamic uncertainties through robust adaptive dynamic programming, policy iteration, and small gain techniques [9]. Additionally, a reinforcement learning-based adaptive optimal control approach has been proposed to learn the optimal control gain of the model to solve traffic congestion [10]. These approaches have been extended to solve robust optimal output regulation problems of a class of partially linear systems under both dynamic uncertainties and denial-of-service attacks [11]. Furthermore, an adaptive tire cornering stiffness strategy and a trajectory-tracking autonomous steering control strategy have been proposed for intelligent vehicle trajectory tracking on different road surfaces, including when affected by inclement weather such as rain and snow [12]. ADP has been developed both in theory and practice, leading to a growing interest in its potential applications in various control systems. The problem of attitude control for hypersonic vehicles in the presence of actuator faults and unknown uncertainties, including modeling uncertainties and aerodynamic parameter uncertainties, has been investigated using the integral sliding mode technique [13]. ADP has shown significant potential in the aerospace field, including investigating fixed-time attitude coordinated control problems for multi-spacecraft systems with unknown external disturbances [14]. The design of intelligent controllers has been widely applied in various fields. A new approach for energy management in photovoltaic (PV)/battery/fuel cell (FC) systems has been presented, which compensates for uncertainties using a proposed deep-learning type-2 fuzzy logic compensator (T2 FLC) and optimization rules based on the immersion and invariance (I&I) theorem, resulting in acceptable voltage/energy regulation performance under different disturbances and without relying on mathematical models [15]. A novel observer-based fuzzy control method for chaotic systems was presented in this study utilizing a generalized type-2 fuzzy logic system to approximate uncertainties and adjust parameters through robustness investigation for improved control performance [16]. The vehicle dynamic behavior under the influence and effects of an antiroll bar mechanism is understood through a basic vehicle dynamic modeling with four DOFs on a half-car model, which shows how external forces on the front antiroll bar can control the handling and ride comfort of the car [17].