Unmanned Autonomous Helicopter Research Articles

Adaptive sensor fault tolerant control with prescribed performance for unmanned autonomous helicopter based on neural networks

PurposeThis paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.Design/methodology/approachTo ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.FindingsThe developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.Originality/valueThe tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

Switching‐like event‐triggered tracking control for UAH/UGV systems under external disturbance and DoS attacks

AbstractIn this article, by considering the existence of unavailable state, external disturbance, and denial of service (DoS) attacks, an anti‐disturbance trajectory tracking controller is proposed for the air‐ground system composed of unmanned autonomous helicopter (UAH) and unmanned ground vehicle (UGV). Initially, by combining the advantages of conventional event‐triggered scheme (ETS) and memory ETS, a switching‐like event‐triggered mechanism is put forward, which can induce less data transmissions without reducing control performance, and effectively restrain the DoS attacks. Second, by dividing the DoS attacks into active intervals and sleep ones, the concept of acknowledgement character technology (ACK) is presented to define the suitable types of time intervals. Third, a switching‐like dynamic ETS is introduced for the sleep intervals of DoS attacks while the ETS with fixed triggered‐threshold is exploited for the active intervals. Fourth, based on the occurrence of DoS attacks, a switching‐like observer and a normal one are respectively proposed to estimate the UGV state, the UAH state, the reference input of the UGV, and the disturbance of the UAH, which are further utilized to design the tracking controller. Fifth, an augmented closed‐loop system consisting of observation errors and tracking error is established and its stability is analyzed by using Lyapunov stability theory. Then, a sufficient condition on co‐designing the parameters of switching‐like ETS, observers, and tracking controller is presented in terms of linear matrix inequalities (LMI). Finally, the validity and superiority of the proposed control scheme are verified by resorting to simulations and comparisons.

Path Planning of Unmanned Autonomous Helicopter Based on Hybrid Satisficing Decision-Enhanced Swarm Intelligence Algorithm

Path Planning of Unmanned Autonomous Helicopter Based on Hybrid Satisficing Decision-Enhanced Swarm Intelligence Algorithm

Trajectory planning and prescribed performance tracking control based on fractional-order observers for unmanned helicopters with unmeasurable states

The trajectory planning and tracking control problem is studied in this paper based on prescribed performance method (PPM) for the small-scale unmanned autonomous helicopter (UAH) with wind-gust disturbances (WGDs) and unmeasurable states. For the purpose, the nonlinear model with flapping dynamics is established, and the transformation performance function is used to ensure that the errors of trajectory tracking satisfy the corresponding performance. A fractional-order observer is designed for unmeasurable states to estimate the flapping angles in actual flight, and the fractional-order extended state observer (ESO) is constructed to estimate the WGDs, respectively. On the basis of PPM and the designed observers, the fractional-order theory-based backstepping trajectory tracking control scheme is developed for the UAH system, and the three-dimensional trajectory is planned by the improved wolf pack algorithm. Then the stability of the entire system is proven through strict theoretical analysis. Finally, the simulation analysis on the UAH is presented to demonstrate the efficiency of the designed method.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration

This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.

Integral Backstepping Sliding Mode Control for Unmanned Autonomous Helicopters Based on Neural Networks

In this paper, we propose an adaptive control approach to deal with the problems of input saturation, external disturbances, and uncertainty in the unmanned autonomous helicopter system. The dynamics of the system take into account the presence of input saturation, uncertainty, and external disturbances. Auxiliary systems are built to handle the input saturation. The neural networks are applied to approximate the uncertain terms. The control scheme combining integral backstepping and sliding mode control is developed in position and attitude subsystems, respectively. In the closed-loop system, the boundedness of the signals is proved by means of the Lyapunov theory. The simulation demonstrates that the approach has good robustness and tracking performance.

Disturbance-Observer-Based Adaptive Fuzzy Tracking Control for Unmanned Autonomous Helicopter With Flight Boundary Constraints

In this article, a disturbance-observer-based adaptive fuzzy tracking control scheme is proposed for a medium-scale unmanned helicopter of six degrees of freedom in the presence of system uncertainties, flight boundary constraints, and external disturbances. A flight boundary protection algorithm is proposed to ensure its flight trajectory within the given safety range. A fuzzy logic system is utilized to estimate the system uncertainties and a nonlinear disturbance observer is adopted to handle the unknown compound terms of the external disturbances and the estimation errors resulting from the fuzzy logic system. An inverse optimal control approach is then used to avoid solving the Hamilton–Jacobi–Bellman equation in minimizing a cost function in the attitude loop. It is shown via the Lyapunov method that the desired safe tracking performance of the position loop and attitude loop of the controlled unmanned helicopter can be achieved. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

Hybrid event-triggered tracking control for unmanned autonomous helicopter under disturbance and deception attacks

This paper studies the hybrid event-triggered tracking control based on an unmanned autonomous helicopter (UAH) and an unmanned ground vehicle (UGV) under unmeasurable states, transmission delay, external disturbance, and deception attacks. By designing a switching condition, a hybrid event-triggered scheme (ETS) is proposed to realize an elegant trade-off between less data transmissions and control performance. Then, based on the designs of the observer and tracking controller, an augmented closed-loop system is established, and a sufficient condition on uniform ultimate boundedness is presented. Moreover, a co-design method of the hybrid ETS, observer, and controller is obtained via linear matrix inequalities (LMIs), which is further extended by utilizing an improved adaptive hybrid ETS. Finally, some simulation and comparisons are exploited to illustrate the proposed method.

Multiapproximator-Based Fault-Tolerant Tracking Control for Unmanned Autonomous Helicopter With Input Saturation

In this article, an adaptive neural fault-tolerant control (FTC) scheme is proposed for the medium-scale unmanned autonomous helicopter subject to external disturbance, actuator fault, and input saturation. Multiple approximators are constructed to handle the unknown terms and promote the control design. The nonlinear coupled function terms are approximated by virtue of the radial basis function neural networks. The unknown disturbance is tackled by the developed disturbance observer. Meanwhile, two auxiliary systems are introduced to handle the actuator fault and input saturation, respectively. In the framework of the backstepping method, a multiapproximator-based adaptive FTC strategy is presented, which assures the boundedness of all closed-loop system signals. Simulation results are presented to validate the availability of the designed controller.

Disturbance observer-based robust coordination control for unmanned autonomous helicopter slung-load system via coupling analysis method

This work studies the robust coordination control for a strong coupled and under-actuated unmanned autonomous helicopter (UAH) slung-load system subject to external disturbances. Firstly, disturbance observers are designed to obtain the disturbance estimations, and a dynamic coupling matrix is introduced to describe the coupling relationships between the UAH system and the slung-load system. Secondly, based on the proposed coupling matrix, a disturbance observer-based coordination control scheme is designed for the position system of the UAH and the slung-load one by using hierarchical sliding mode control. Thirdly, an improved controller is designed for the attitude system of the UAH by combining the backstepping control, disturbance observer, and dynamic surface control, in which the detrimental coupling is suppressed while the beneficial one is retained to improve control performance. Especially, the stability for the overall closed-loop system is guaranteed based on Lyapunov stability theory. Finally, some simulations are presented to show the effectiveness and advantage of the proposed control scheme.

Disturbance Observer-based LQR Tracking Control for Unmanned Autonomous Helicopter Slung-load System

Disturbance Observer-based LQR Tracking Control for Unmanned Autonomous Helicopter Slung-load System

Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning

Aiming at producing a high-quality flight path for the unmanned autonomous helicopter with multi-constraints, a path planning method is proposed based on the multi-strategy evolutionary learning artificial bee colony algorithm in this paper. Firstly, an evolutionary learning framework is established for the artificial bee colony algorithm based on brain-like cognition. By integrating the swarm intelligence and human cognitive mechanism, this framework gives more autonomy and intelligence to the bee colony. In addition, a multi-strategy evolutionary database is built based on the evolutionary learning framework to replace the traditional evolutionary approach of the artificial bee colony algorithm. Different nectar sources adopt different evolutionary strategies according to the integrated feedback mechanism, and evolutionary behavioral selection probability is updated through the accumulation of experience and the exploration of new knowledge. The simulation results show that the trajectories produced by the multi-strategy evolutionary learning artificial bee colony algorithm have better fuel economy and higher safety than other comparison algorithms, and the number of optimization iterations can be reduced by at least 12%.

Adaptive tracking flight control for unmanned autonomous helicopter with full state constraints and actuator faults

In this study, a robust adaptive flight controller is developed to achieve anticipant trajectory tracking performance for a medium-scale unmanned autonomous helicopter (UAH) with external disturbances, actuator faults and full state constraints. The flapping dynamics of the main rotors is taken into consideration in the modeling process. The adaptive method is employed to handle the unknown bounded disturbances. A novel fault tolerant control (FTC) strategy is proposed to cope with actuator faults, which can effectively overcome the singularity problem in many adaptive estimation approaches by using projection function. Meanwhile, a suitable barrier Lyapunov function (BLF) is constructed to refrain from the violation of the state constraints. Combining with the backstepping technique, the four control inputs of the medium-scale UAH system are designed in sequence. Via the Lyapunov analysis, it is proved that all error signals in the closed-loop system converge to a small neighborhood around zero. Simulation studies are carried out to reveal the validity of the presented approach.

Adaptive tracking control for an unmanned autonomous helicopter using neural network and disturbance observer

In this paper, an adaptive tracking control scheme is investigated for a medium scale unmanned autonomous helicopter (UAH) with unknown external disturbances and system uncertainties to achieve improvement on the flight performance. The neural networks (NNs) are employed to compensate the system uncertainties. The second-order disturbance observers are introduced to restrain the compound disturbances which are combined with the NN approximation errors and the external disturbances. Accordingly, the tracking control law is designed for the UAH. The closed-loop stability of the whole UAH system is proved by using Lyapunov function method. Simulation results show that the developed control scheme can effectively solve the tracking control problems of UAH and certainly accomplish strong robustness with respect to the external disturbances and system uncertainties.

Fault identification and fault-tolerant control for unmanned autonomous helicopter with global neural finite-time convergence

In this paper, the issue of global neural finite-time fault-tolerant control (FTC) is investigated for the medium-scale unmanned autonomous helicopter (UAH). To recognize the actuator bias and loss of effectiveness (LOE) faults, a novel fault detection and identification (FDI) strategy is proposed, which consists of a fault detection observer, two adaptive fault observers and a decision-making algorithm. The neural network (NN) technique is employed to deal with the unknown system uncertainty. In view of the backstepping approach and Lyapunov theory, a finite-time FTC scheme is developed to assure that all closed-loop system tracking errors converge to a small range of zero after a limited amount of time. Meanwhile, by integrating a switching mechanism into the control design, the traditional semi-globally uniformly ultimately bounded (SGUUB) stability is extended to globally uniformly ultimately bounded (GUUB) stability, such that the constraints on initial conditions of the NN controller is moderated. Simulation studies are implemented to demonstrate the usefulness of the presented controller.

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

Switched safe tracking control design for unmanned autonomous helicopter with disturbances

In this paper, the switched safe tracking control scheme is investigated for the attitude and altitude system of a medium-scale unmanned autonomous helicopter with output constraints and unknown external disturbances. To keep the attitude angles and altitude within the desired constrained range, an output boundary protection approach is adopted to generate an output constrained trajectory which is piecewise differentiable. The disturbance observer-based control method is employed to handle the unknown external disturbances of the system. Because of the piecewise differentiability of the output constrained trajectory, the closed-loop error system with the safe tracking controller can be seen as a switched system with jump dynamics. The multiple Lyapunov function method is adopted to guarantee the tracking performance with designed average dwell time. Simulation results of an example are provided to illustrate the effectiveness of the proposed control scheme for the unmanned autonomous helicopter system.

Two time‐scale assignment with state extension for an autonomous helicopter

AbstractIn this paper, a feedback control law based on two time‐scale assignment and state extension is implemented for the nonlinear non‐minimum phase dynamics of three degree‐of‐freedom autonomous unmanned helicopters (AUH). Unlike previous techniques, the design procedure does not require time‐scale decomposition between attitude angle and translational states. This reduces the requirements for the AUH model and makes the controller more reliable. Time‐scale assignment is implemented with partial internal dynamics. Exponential stability of the closed‐loop system is proved with the retained non‐minimum phase character, and simulation results are provided to demonstrate the effectiveness of the presented approach.

Adaptive flight control for unmanned autonomous helicopter with external disturbance and actuator fault

A backstepping-based robust adaptive fault tolerant control (FTC) scheme is proposed for the altitude and attitude dynamic model of the medium-scale unmanned autonomous helicopter (UAH) subjected to external disturbance and actuator fault. The external disturbance and actuator fault are integrated into the medium-scale UAH altitude and attitude dynamic model. Since the actuator fault is uncertain in occurring time instant, the adaptive control approach is adopted to compensate for its negative effect. Meanwhile, the upper bound of external disturbance is estimated and the estimation is employed to design the controller. Based on the backstepping control technique, a robust adaptive FTC law is developed, which guarantees the desired system stability as well as the satisfactory tracking property. Simulation studies verify the effectiveness of the designed controller.