Optimal Attitude Control Research Articles

Fabrication Time Diagrams for In-Space Manufacturing of Large Reticulated Structures

Abstract In-space manufacturing (ISM), or the on-orbit construction of structures from raw feedstock materials, is a promising approach for building large structures for future space missions. Most research and development on ISM to date has focused on ground-based or microgravity-based demonstrations of candidate fabrication processes; however, the combined design of the fabrication process and the ISM spacecraft has not been fully investigated. In this paper, we estimate the fabrication times for ISM of reticulated support structures subject to various spacecraft constraints, including the available fabrication power, the available attitude control authority, and the avoidance of undesirable control-structure interactions. We summarize the insights in fabrication time diagrams which depict the dominant constraints and estimates of the fabrication time for a wide range of structural geometries. Our results indicate that for large, dense structural geometries such as the curved gridshell and tetrahedral truss, the angular momentum storage of the ISM spacecraft's attitude control system (ACS) is the dominant constraint on fabrication time. Additionally, our results suggest the following strategies for reducing fabrication time: manufacturing with multiple spacecraft; using stiff, lightweight feedstock; maximizing fabrication power and ACS capability; and minimizing spacecraft bus mass. Altogether, our results emphasize how the design of an ISM spacecraft cannot be considered independently of the fabricated structure and motivate future work on selecting optimal fabrication paths and attitude control strategies to fabricate large structures in the shortest time.

Energy Optimal Attitude Control and Task Execution for a Solar-Powered Spacecraft

Energy Optimal Attitude Control and Task Execution for a Solar-Powered Spacecraft

Saturated Trajectory Tracking Controller in the Body-Frame for Quadrotors

This paper introduces a quadrotor trajectory tracking controller comprising a steady-state optimal position controller with a normed input saturation and modular integrative action coupled with a backstepping attitude controller. First, the translational and rotational dynamical models are designed in the body-fixed frame to avoid external rotations and are partitioned into an underactuated position system and a quaternion-based attitude system. Secondly, a controller is designed separately for each subsystem, namely, (i) the position controller synthesis is derived from the Maximum Principle, Lyapunov, and linear quadratic regulator (LQR) theory, ensuring the global exponential stability and steady-state optimality of the controller within the linear region, and global asymptotic stability is guaranteed for the saturation region when coupled with any local exponential stable attitude controller, and (ii) the attitude system, with the quaternion angles and the angular velocity as the controlled variables, is designed in the error space through the backstepping technique, which renders the overall system, position, and attitude, with desirable closed-loop properties that are almost global. The overall stability of the system is achieved through the propagation of the position interconnection term to the attitude system. To enhance the robustness of the tracking system, integrative action is devised for both position and attitude, with emphasis on the modular approach for the integrative action on the position controller. The proposed method is experimentally validated on board an off-the-shelf quadrotor to assess the resulting performance.

Optimal attitude control for landing on asteroid with a flexible lander

The recently developed flexible lander offers a potential option for future asteroid landing missions due to its advantages in suppression of rebound and overturning. The strong nonlinear dynamics and control constraints of the flexible lander hinder the online implementation of optimal attitude feedback control. To generate the optimal attitude control rapidly, a constrained inhomogeneous approximating sequence of Riccati equations (CI-ASRE) method is proposed. The saturation function is firstly constructed to incorporate control magnitude constraints into dynamics so that the constraints can be analytically handled. Subsequently, to avoid singularity, a partial factorization strategy is proposed to convert the constrained dynamics into two parts, a pseudolinear term and an inhomogeneous term. This factorization enables the nonlinear problem to be expressed as the limit of a sequence of inhomogeneous linear quadratic problems while avoiding local linearization. At last, the analytical CI-ASRE is newly derived to iteratively solve these problems to rapidly obtain optimal feedback control. The computational simplicity and effectiveness of the CI-ASRE method overcome difficulties of optimal attitude control problems of the flexible lander associated with control constraints, strong nonlinearity, and online feedback control generation. The effectiveness of the proposed method for the flexible lander is verified using a landing scenario on asteroid 433 Eros.

Distributed Approximate Optimal Attitude Control of Combined Spacecraft With Parameters Identification

Distributed Approximate Optimal Attitude Control of Combined Spacecraft With Parameters Identification

Fixed-Time Fault-Tolerant Optimal Attitude Control of Spacecraft With Performance Constraint via Reinforcement Learning

Fixed-Time Fault-Tolerant Optimal Attitude Control of Spacecraft With Performance Constraint via Reinforcement Learning

Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.

The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning

Efficient control of tunnel boring machine (TBM) tunneling along the designed tunnel axis in an unknown variable geological environment is a difficult and significant task. At present, the TBM attitude during tunneling is mostly manually controlled based on the deviation between the tunneling axis and the designed tunnel axis and their experiences. The tunneling axis from manual control is often the snakelike motion around the designed tunnel axis, even exceeding the deviation limit, for which this paper analyzed three reasons, the unknown geological environment, the hysteresis of TBM position response, and the unsolved overall optimization of tunneling axis. For these reasons, this paper proposed a real-time optimal control framework of TBM attitude based on reinforcement learning, which contains the geological information predictive model, TBM attitude and position (TBMAP) predictive model, and optimal attitude control policy (OACP). This framework can predict the current geological information in real-time and provide the corresponding real-time optimal attitude control that simultaneously considers the hysteresis of TBM position response and the overall optimization of the tunneling axis. This attitude control framework can be directly deployed to TBM without increasing costs and excessive modifications to the equipment. To verify the effectiveness of this attitude control framework, the Xinjiang Yiner Water Supply Phase II Project, using the TBM method, was adopted as a case study. The results revealed that the accuracy of geological environment recognition reached 94%, and OACP can significantly reduce the accumulated deviation of the tunneling axis from the designed tunnel axis by over 80% compared with the manual control and easily provide real-time decision support for attitude control in actual engineering.

ADCS Design for a Sounding Rocket with Thrust Vectoring

This paper addresses the development of an attitude determination and control system (ADCS) for a sounding rocket using thrust vector control (TVC). To design the ADCS, a non-linear 6 degrees-of-freedom (DoF) model for the rocket dynamics and kinematics is deduced and implemented in simulation environment. An optimal attitude controller is designed using the linear quadratic regulator (LQR) with an additional integral action (LQI), and relying on the derived linear, time-varying, state-space representation of the rocket. The controller is tested in the simulation environment, demonstrating satisfactory attitude tracking performance, and robustness to model uncertainties. A navigation system is designed, based on measurements available on-board, to provide accurate real-time estimates on the rocket’s state and on the aerodynamic forces and moments acting on the vehicle. These aerodynamic estimates are used by an adaptive version of the controller that computes the gains in real time after correcting the state-space model. Finally, the ADCS is the result of the integration of the attitude control and navigation systems, with the complete system being implemented and tested in simulation, and demonstrating satisfactory performance.

Uncertain optimal attitude control for space power satellite based on interval Riccati equation with non-probabilistic time-dependent reliability

Considering the multi-source uncertainties and disturbances in the attitude dynamics of space power satellite (SPS), an interval dynamic model of SPS is proposed based on the non-probabilistic theory, which is controlled by a novel uncertainty-based linear–quadratic regulator (LQR) method with time-dependent reliability. Based on the interval analysis method, an interval time-varying attitude dynamic model of SPS for sun-pointing is established considering the uncertain inertia and initial states as unknown-but-bounded parameters. Feedforward and feedback controllers are designed to offset the disturbance and control attitude, respectively. An interval Riccati equation-based optimal control method is proposed to develop a novel uncertainty-based LQR method. Non-probabilistic time-dependent reliability is constituted based on the interval process and first passage theory to assess attitude states. An uncertain multi-objective optimization of attitude control with reliability constraints is proposed to realize the uncertain LQR method. Verified by a numerical example of SPS, the proposed method takes only 0.6‰∼0.8‰ time consumption of 10,000 Monte Carlo simulations while maintaining a certain accuracy.

Event-Triggered Adaptive Hybrid Torque-Position Control (ET-AHTPC) for Robot-Assisted Ankle Rehabilitation

Ankle rehabilitation for increasing number of strokes is highly demanded, and robot-assisted approach has shown great potential. Since the required movement and force assistances will concurrently change during rehabilitation sessions, the robotic assistances are supposed to be adjusted accordingly. In order to achieve both adaptive torque and synchronous position control for the robot in practice, a novel event-triggered adaptive hybrid torque-position control (ET-AHTPC) is proposed for a developed ankle rehabilitation robot driven by pneumatic muscles (PM). In the novel adaptive torque control scheme, the assistive torque adapted to the patient's recovery state is adjusted by a designed robot-assisted rehabilitation index mapping from the clinical assessment scale. The robotic assistance output is online corrected by patient's performance, based on a correcting index calculated by interaction torque and tracking errors. Then a model-based event-triggered optimal position controller is established and a critic neural network (NN) is introduced to reduce the control law update frequency for fast trajectory tracking. The stability of the overall system is proved by Lyapunov theorem. A series of experiments were conducted on the ankle rehabilitation robot to validate the controller's fast trajectory tracking and adaptive assistance capacity, which can online adjust the robot's assistive torque and allowable movement range for patients at different recovery stages.

Finite-time prescribed performance optimal attitude control for quadrotor UAV

In this paper, the optimal attitude control problem is investigated for quadrotor UAV with time-varying control effectiveness faults based on adaptive dynamic programming. Initially, a novel nonlinear conversion function is introduced to achieve the finite-time prescribed performance on tracking errors of three attitude angles of the quadrotor UAV. Furthermore, given that the controller may experience a degree of time-varying faults due to internal component aging or external environmental influences, an improved value function with a discount factor is proposed to resist the aforementioned time-varying control effectiveness faults. Then, by integrating the concurrent learning technique into adaptive dynamic programming algorithm, a valid approximate optimal control protocol is presented. Such an algorithm not only effectively improves the utilization of historical data in the history stack but also loosens the strict persistent excitation condition into a rank condition that is easy to check online. Moreover, it can be proved that the uniformly ultimately bounded stability of the attitude system is ensured even in the presence of time-varying control effectiveness faults. Meanwhile, tracking errors of attitude angles successfully converge to pre-defined symmetric or asymmetric precision intervals with an adjustable convergence rate in a user-defined finite time. Finally, a simulation example of the quadrotor UAV attitude system is given to verify the validity of the developed control scheme.

Interval Uncertainty-Oriented Optimal Control Method for Spacecraft Attitude Control

Research on uncertainty-oriented optimal attitude control of spacecraft with complex space environments and multi-source uncertainties is a research hotspot. Considering that the uncertain parameters in the control system are difficult to quantify, this study proposed an interval uncertainty-oriented optimal control method based on the linear quadratic regulator (LQR) for spacecraft attitude control. The interval state-space equation of the spacecraft attitude dynamic with uncertain controlled feedback gain was constituted by expanding the deterministic model into an order-extended interval matrix format. Based on the interval uncertainty propagation method, the interval-based Riccati equation in LQR was proposed using the modified interval estimation method. Therefore, the interval-controlled feedback gain and interval cost function could be obtained, and the overestimation attributed to the interval expansion could be avoided. The interval-based reliability was investigated using the state-threshold interference model, and the interval-based safety index was developed. The interval uncertainty-based multi-objective optimal control model with constraints was proposed to balance both minimizations of the optimal control cost function and state vector fluctuation by considering these two interval indices as the constraints in optimal control. A flowchart and a numerical example of satellite attitude control were applied to reflect the effectiveness.

Optimal Position Control of Nonlinear Muscle Based on Sliding Mode and Particle Swarm Optimization Algorithm

Rehabilitation is a way to care muscle and skeleton disorders and disease, using mechanical equipment. The aim of rehabilitation is that help disable persons who they reach to minimum physical activities. Since pneumatic actuators have high respect of power to weight, are cleanliness, reachable fluid, are to repair and low cost that can use to implement in robot which interact with human body parts. The other features that use on pneumatic actuators that this is comprisable fluid and variable stiffness traits. Due to importance and performance of these actuators in medical science and also they have high security and exact control which can preserve bodies. Therefore in this project, we search pneumatic actuators with high precision controller that can manufacture various mechanisms for rehabilitation process. Pneumatic actuators mathematical modeling is presented in this thesis. According to modeling of pneumatic actuator, differential equations of actuators are presented. In this research, we used solenoid valves, because proportional valves are expensive. We used sliding mode theory for position control of actuator. There are many importance on these systems and the main aim of this project, optimal path of 2 links mechanism for rehabilitation problems. So optimal tracking robot, we use particle swarm optimization for finding optimal gain controller in sliding mode control because we want to achieve minimum tracking errors in path planning of mechanism.

Attitude optimization control of hypersonic flight vehicle considering partially unknown control direction

In this paper, a control method based on periodic sliding mode technology and adaptive dynamic programming algorithm is proposed for the attitude dynamics of hypersonic flight vehicle. The method takes into account a case that there is unknown control direction in a single channel. First, under the premise that the disturbance observers estimate the coupling and other disturbances, the attitude dynamics are decomposed into mathematical models of pitch channel and yaw-roll channel, respectively. Then, a periodic sliding mode control law with both stability and rapidity is designed for the unknown control direction in pitch channel. In the yaw-roll channel, an optimal attitude control law satisfying cost function is designed based on adaptive dynamic programming algorithm. What’s more, the closed-loop system is proved to be stable by Lyapunov stability theorem. Finally, the simulation results show that the attitude angle can track the command stably, which proves the effectiveness of the control scheme. Compared with the backstepping control scheme, the simulation results show that the proposed scheme can effectively avoid the divergence of the system because of the control direction switch, and the system have better dynamic performance and robustness.

Robust optimal attitude control for hexarotors with disturbances, uncertainties, and delays

AbstractA robust optimal attitude controller for hexarotor helicopters is proposed. Compared to the previous research studies on hexarotors, the current study takes account of the influences of non‐linear and coupling dynamics, structured and unstructured uncertainties, external time‐varying disturbances, and input time delays. A linear time‐invariant system is derived for each Euler angle by considering the actual rotational dynamic model as a nominal non‐linear system plus an equivalent perturbation, including non‐linear and coupling dynamics, uncertainties, disturbances, and time delays. Using this approach, a Linear Quadratic Regulation controller is first designed for the nominal linear system of each angle to accomplish the desired tracking performances. Then, a robust compensator based on the robust compensation method is proposed to counteract the effects of the equivalent perturbation on the system. Moreover, the robust attitude tracking property and uniform asymptotical stability of the closed‐loop hexarotor system are proved using Lyapunov stability theory. Several simulations have been performed to demonstrate the effectiveness and robustness of the proposed controller. Finally, experimental results are provided to confirm the robust performance of the proposed controller.

Fully Distributed Optimal Position Control of Networked Uncertain Euler-Lagrange Systems Under Unbalanced Digraphs.

The distributed optimal position control problem, which aims to cooperatively drive the networked uncertain nonlinear Euler-Lagrange (EL) systems to an optimal position that minimizes a global cost function, is investigated in this article. In the case without constraints for the positions, a fully distributed optimal position control protocol is first presented by applying adaptive parameter estimation and gain tuning techniques. As the environmental constraints for the positions are considered, we further provide an enhanced optimal control scheme by applying the ϵ -exact penalty function method. Different from the existing optimal control schemes of networked EL systems, the proposed adaptive control schemes have two merits. First, they are fully distributed in the sense without requiring any global information. Second, the control schemes are designed under the general unbalanced directed communication graphs. The simulations are performed to verify the obtained results.

H ∞ inverse optimal attitude tracking on the special orthogonal group SO(3)

The problem of attitude tracking in the presence of disturbances is addressed by combining inverse optimality and disturbance attenuation. Conditions are provided which ensure that an inverse optimal nonlinear attitude control problem is solved and a meaningful cost function is minimised. The approach results in a PD-type attitude control law on the Special Orthogonal Group which guarantees that, for almost all initial conditions, the energy gain from an exogenous disturbance to a specified error signal respects a given upper bound. For numerical simulations, a satellite attitude tracking problem from literature is considered. The controller gains are tuned using a decoupled linearised single-axis model and the structured control synthesis method. Results indicate that the proposed controller has good tracking and disturbance attenuation capability. In particular, for the problem under consideration, the inverse optimal controller is seen to have better transient performance than its continuous-time quaternion counterpart. Moreover, for initial errors up to radians, it has comparable performance to the shortest-path quaternion PD controller.

ADP-Based Spacecraft Attitude Control Under Actuator Misalignment and Pointing Constraints

This article is devoted to real-time optimal attitude reorientation control of rigid spacecraft control. Particularly, two typical practical problems—actuator misalignment and forbidden pointing constraints are considered. Within the framework of adaptive dynamic programming (ADP), a novel constrained optimal attitude control scheme is proposed.In this design, a special reward function is developed to characterize the environment feedback and deal with the pointing constraints. Notably, a novel argument term is introduced to the reward function for overcoming the inevitable difficulty in the actuator misalignment. By virtue of the Lyapunov stability theory, the ultimate boundedness of state error and the optimality of the proposed method can be guaranteed. Finally, the effectiveness and performance of the developed ADP-based controller are evaluated by not only numerical simulations but also experimental tests with a hardware-in-loop platform.

Optimal Position Fuzzy Control of an Underactuated Robotic Finger

In this paper, the optimal position control of an underactuated robotic finger is presented. Two trajectories, one for the proximal and the other for the medial phalanx, are proposed in order to emulate the finger’s flexion/extension movements. A Mandani fuzzy control is proposed due to the lack of a precise dynamical model of the system. In order to obtain the control parameters, an optimization strategy based on the membership functions is applied. Genetic algorithms (GA) are commonly used as an optimization method in diverse applications; however, in this case, the use of an autoadaptive differential evolution method is proposed in order to obtain a superior convergence behavior. Simulations of the virtual prototype are carried out using MATLAB/Simulink software to display the trajectory tracking. The results show that the maximum error between the proposed and obtained trajectories is 3.1352E − 04 rad.