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A composite controller based on a backstepping controller with an adaptive fuzzy logic system and a nonlinear disturbance observer is proposed in this paper to address the disturbance and uncertainty issues in the control of the optoelectronic stabilized platform. The matched and unmatched disturbances and system uncertainty are included in the stabilized platform model. The system's uncertainty and disturbance are approximated and estimated using an adaptive fuzzy logic system and a nonlinear disturbance observer. Moreover, the backstepping control algorithm is utilized to control the system. The simulations are performed in four states to confirm the viability of the proposed control technique. The proportional integral controller, proportional integral-disturbance observer controller, and fuzzy backstepping controller are contrasted with the proposed controller. It has been noted that the proposed controller's instantaneous disturbance's highest value is 5.1°/s. The maximal value of the coupling output for the two gimbals utilizing the proposed controller, however, is 0.0008°/s and 0.0018°/s, respectively. The findings presented here demonstrate that the backstepping controller, which is based on an adaptive fuzzy logic system and a nonlinear disturbance observer, is capable of precise tracking and dynamic tracking of a stabilized platform under disturbance and uncertainty.

It is still an open and challenging issue to the typical position control problems of the three‐axis electrical‐optical gyro‐stabilized platform systems (TEOGSP), due to inherent characteristics, for example, measurement noise, input saturation, parametric uncertainties, largely unknown load disturbance. To solve this problem, a saturated adaptive robust feedback controller using an adaptive cascaded extended state observer (SAFCESO) is proposed for compromising between the measurement noise effect and the sensitivity to disturbances. Firstly, the matched and mismatched disturbances existing in the TEOGSP system are estimated and rejected by the cascaded adaptive extended state observers (CESO). Secondly, the parametric uncertainties are evaluated by the adaptive control, and the match disturbances are attenuated by the robust control. Moreover, the adaptive robust control law does not require the velocity measurement signal and internal dynamics information of the system, which is practical to implement. Hence, all various uncertainties could be mainly compensated. Then, the improved auxiliary systems governed by smooth switching functions are developed and incorporated into the control design to compensate for the effect of the input saturation. Finally, the command filters are introduced to limit the magnitude of the virtual control and to calculate the derivative of the virtual control, respectively. The extensive comparative experimental results in the TEOGSP systems showed that the proposed SAFCESO method had superiorities in terms of high‐precision tracking accuracy, robustness, and noise reduction.

•Adaptive binary controller via output feedback for exact tracking of multivariable uncertain plants with nonuniform arbitrary relative degrees.•Multivariable generalization of the global finite-time differentiators with dynamic gains and higher-order sliding modes.•Global asymptotic stability of the closed-loop system and ultimate exponential convergence to small residual sets are guaranteed.•Fast transient responses, improved tracking precision and chattering-free control signals.•Engineering application to inertially stabilized platforms with numerical results based on data acquired from experiments in real-world conditions. This paper presents an extension of the Binary Model Reference Adaptive Control (BMRAC) for uncertain multivariable (square) systems with non-uniform arbitrary relative degrees using only output feedback and its application to inertially stabilized platforms using a two degree of freedom gimbal as actuator. The BMRAC is a robust adaptive strategy with good transient performance, thus useful for uncertain systems, and the multivariable framework is suitable to deal with mechanical unbalances. Using a newly proposed differentiator with dynamic gains based on higher-order sliding mode, the proposed controller achieves global and exact tracking. To illustrate the effective of the proposed solution, simulations are presented using real-word data obtained from an instrumented vehicle in an irregular ground.

Aiming at the control challenges of strong nonlinearity, time-varying parameters and multi-channel disturbance coupling in multi-address laser communication networking caused by the common inertial reference of multi-directional mirror strapdown stabilized platforms, a genetic algorithm-optimized active disturbance rejection control (GA-ADRC) method is proposed. By constructing a distributed active disturbance rejection control (ADRC) architecture and using genetic algorithms to globally and collaboratively optimize the observer gain and control parameters, the disturbance suppression and dynamic decoupling of multi-variable systems are effectively achieved. Experimental results show that under 0.1–0.3 Hz base disturbances, this method improves the line of sight (LOS) stabilization accuracy by 28–32%, with a standard deviation better than 14 μrad, significantly outperforming traditional PID control. This research not only provides a high-accuracy control solution that does not rely on precise models for multi-LOS cooperative stabilization but also offers a generalizable theoretical and practical framework for the intelligent control of complex optoelectronic systems.

As the imaging distance and focal length of photoelectric systems increase, the requirements for line-of–sight stabilization of optical inertial stabilized platforms (ISPs) become higher. Disturbance rejection directly determines the stability accuracy of optical inertial stabilized platforms. However, the accurate observation and suppression of wide-band and rapidly changing disturbances remains a challenge in current engineering applications. This paper proposes a robust extended sliding mode observer (ESMO) method to improve disturbance estimation performance. First, the linear extended state observer (LESO) is designed by taking the total disturbances as extended states. Then, a sliding mode observer (SMO) is incorporated in the extended states of the extended observer, forming a robust ESMO. Subsequently, the robustness and convergence characteristics of the proposed method are mathematically proved, revealing that it operates robustly without knowing the disturbance’s upper bound and offers faster dynamics and higher accuracy than the LESO. Finally, a series of simulation experimental tests are performed to demonstrate the effectiveness of the proposed method. The proposed method observes wide-band and rapidly changing disturbances utilizing the rapidly switching characteristic of the SMO and smooths the jitter of the SMO by cascading sliding mode estimation to the differentiation term of extended observation, achieving the integral effect of the reaching law. Meanwhile, this method only requires adjusting two parameters, making it suitable for engineering applications. It can be effectively used in optical inertial stabilized platform control systems for disturbance estimation and compensation.

A multi-degree-of-freedom (multi-DOF) control method is proposed in this paper for the optoelectronic platform affected by internal and external disturbances. First, internal model control (IMC) is used to track the desired signal, and combined with radial basis function neural network (RBFNN)-based sliding mode control (SMC) to compensate for friction torque and weaken model uncertainty. Then, linear active disturbance rejection control (LADRC) is introduced to observe and compensate for sensor noise as well as external unknown disturbances, so that the optoelectronic platform can operate under complex working conditions. The input and disturbance sensitivity functions in a pure feedback control system cannot reach their minimum values in the same frequency band, so there is an inherent contradiction between their tracking and disturbance rejection performance. Combining IMC-SMC-RBFNN with LADRC as a multi-DOF controller can guarantee both tracking and disturbance rejection performance. Lyapunov theory and Barbalat lemma prove the asymptotic stability of the control system. Simulations show that the multi-DOF controller has a good control effect under the mixed disturbances such as parameter perturbation, friction torque and sensor noise, which has reference value for the development of practical optoelectronic platform systems.

To realize the stable tracking control of the optoelectronic stabilized platform system under nonlinear friction and external disturbance, an active disturbance rejection controller (ADRC) with friction compensation is proposed to improve the target tracking ability and anti-disturbance performance. First, a nonlinear LuGre observer is designed to estimate friction behavior and preliminarily suppress the interference of friction torque on the system. Then, an ADRC is introduced to further suppress the residual disturbance after friction compensation, and the stability of the ADRC system is also proved. The effectiveness of this scheme is proved by simulation experiments, and this scheme is compared with conventional ADRC and LuGre friction feedforward compensation. The simulation results show that an ADRC with LuGre friction compensation is better with trajectory tracking performance, which suppresses the influence of disturbance and improves the stability of the optoelectronic stabilized platform system.

Symmetry is presented in the frame structure, modeling, and disturbance analysis of the three-axis inertially stabilized platform (ISP), which affects the control performance of the ISP. To realize high-performance control for the ISP, a nonlinear dynamic model based on the geographic coordinates and a compound control method based on the adaptive extended state observer (ESO) and adaptive back-stepping integral sliding mode control (SMC) are proposed. The nonlinear dynamic model based on geographic coordinates could avoid the degradation of measurement and control performance due to complex coordinate transformations. An adaptive ESO (AESO) has been developed to estimate the unknown disturbances of ISP. With the information from the ISP system, the adaptive bandwidth of AESO can deal with the peaking phenomenon without introducing excessive noise. Furthermore, based on the integral sliding mode, the adaptation laws of parameter uncertainty and disturbance estimation compensation have been developed for the back-stepping integral SMC method, which can reduce the estimation burden and improve the disturbance estimation accuracy of AESO. The asymptotic stability of the compound control method has been proven by the Lyapunov stability theory. Through a series of simulations and experiments, the effectiveness of the compound method is validated.

The three-degrees-of-freedom (3-DOF) parallel robot is commonly employed as a shipborne stabilized platform for real-time compensation of ship disturbances. Pose accuracy is one of its most critical performance indicators. Currently, neural networks have been applied to the kinematic calibration of stabilized platforms to compensate for pose errors and enhance motion accuracy. However, collecting a large amount of measured configuration data for robots entails high costs and time, which restricts the widespread use of neural networks. In this study, a “transfer network” is established by combining fine-tuning with a Back Propagation (BP) neural network. This network takes the motion transmission characteristics inherent in the ideal kinematic model as prior knowledge and transfers them to a network trained based on the actual poses. Compared with the conventional BP neural network trained by actual poses alone, the transfer network shows significant performance advantages, effectively solving the problems of low prediction accuracy and weak generalization ability in the case of small-sample measured data. Considering this, the impact pattern of the sample number of the actual pose on the effectiveness of transfer learning is revealed through the construction of multiple transfer network models under varying sample numbers of the actual pose, providing valuable marine engineering guidance. Finally, simulated sea-service experiments were conducted on the 3-UPS/S shipborne stabilized platform to validate the correctness and superiority of the proposed method.

The nonlinear friction modeling and feed-forward compensation of the velocity-stabilized loop in inertially stabilized platform and closed-loop control system are studied in this paper. In order to obtain higher precision performance, an improved Stribeck friction model is proposed and designed according to the actual experimental data, whose parameters are identified by the genetic algorithm. The feed-forward compensation strategy is based on the improved model. The chattering problem and limit cycle, which arise from the changes of motion directions and the over compensation of the friction, are avoided by optimizing the compensation strategy. The actual experimental results demonstrate that the isolation performances of tracking system and carrier turbulence isolation system are superiority to the corresponding control systems without the compensations of nonlinear friction model proposed. This work has a great significance in actual applications.