Search results

This study aims to design a controller which can improve the end-effector low-frequency chattering resulting from the measurement noise and the time delay in the on-orbit tasks. The rendezvous point will move along the rendezvous ring owing to the error of the camera, and the manipulators’ collision need be avoided. In addition, owing to the dynamics coupling, the manipulators’ motion will disturb the spacecraft, and the low tracking accuracy of the end-effector needs to be improved.

This paper proposes a minimum disturbance controller based on the synchronous and adaptive acceleration planning to improve the tracking error and the disturbance energy. The synchronous and adaptive acceleration planning method plans the optimal rendezvous point and designs synchronous approaching method and provides an estimation method of the rendezvous point acceleration. A minimum disturbance controller is designed based on the energy conservation to optimize the disturbance resulting from the manipulator’s motion.

The acceleration planning method avoids the collision of two end-effectors and reduces the error caused by the low-frequency chattering. The minimum disturbance controller minimizes the disturbance energy of the manipulators’ motion transferred to the spacecraft. Experiment results show that the proposed method improves the low-frequency chattering, and the average position tracking error reduces by 30%, and disturbance energy reduces by 30% at least. In addition, it has good performances in the synchronous motion and adaptive tracking.

Given the immeasurability of the target satellite acceleration in space, this paper proposes an estimation method of the acceleration. This paper proposes a synchronous and adaptive acceleration planning method. In addition, the rendezvous points are optimized to avoid the two end-effectors collisions. By the energy conservation, the minimum disturbance controller is designed to ensure a satisfying tracking error and reduce the disturbance energy resulting from the manipulators’ motion.

The purpose of this paper is to resolve the problem of the dynamic response performance of the driving control system for a six-wheeled planetary rover. An adaptive sliding mode controller based on an improved genetic algorithm (IGA) to tune PID sliding surface parameters was used in the driving control system of the planetary rover.

First, the mathematical model of planetary rover driving control is established. Second, according to sliding mode variable structure control, an equivalent controller and a disturbance controller are constructed to solve the problem of a multi-disturbance nonlinear driving control system of planetary rovers and an IGA is used to tune PID parameters.

Simulation results show that the proposed control algorithm improves the accuracy of the driving control system and optimizes the smoothness of rover motion control.

The controller based on the IGA to tune PID sliding surface parameters has good self-adaptability and real-time controllability for the control object which is difficult to present a precise mathematical model.

The advanced control method is adopted to solve the uncertainty and external interference of planetary rovers in a complex environment. The mathematical model of the six-wheeled rover is established as the control object and the uncertainty and external disturbance of the model are considered. The controller based on IGA has good adaptability and real-time performance and the control algorithm can be used to drive robots in complex environments.

Nowadays, in order to improve current applications, industry incorporates to their solution approaches artificial intelligence techniques and methodologies like Fuzzy Logic, neural networks and/or genetic algorithms (GA). Artificial intelligence techniques complement classical methodologies and include concepts that simulate the way humans solve problems or how processes work in nature. In this work, the Fuzzy Logic system cancels the effects of load perturbances in an energy plant, by implementing a secondary controller which complements the main controller. The purpose of this paper is to use GA to tune this new secondary controller. The authors particularize the proposal for three specific applications: control the angular speed and position of a Direct Current (DC) motor and control the output voltage of a DC/DC buck converter.

The authors use GA for tuning a Proportional-Integral Fuzzy Controller (PI-Fuzzy). The proposal defines a new objective function in comparison with literature approaches. The main key in the new objective function is combining the best features of Integral Square Error (ISE) function and taking out the overshoot response.

In order to demonstrate the proposed methodology based on GA tuning a PI-Fuzzy, the authors apply the literature benchmark to the solution. The results are compared with the following techniques: Robust control, continuous PID control, discrete PID control, Optimal Control, Fuzzy Control and Artificial Neural Network based control. Comparisons are presented in terms of setting time and overshot.

Results demonstrate that ISE or integral of absolute value of error function do not provide the desired response. Achieved results demonstrate the usefulness of the proposal to eliminate the overshoot of the traditional behaviour without lost any of the main features of the literature methodologies.

Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices.

In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller.

The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system.

The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems.

Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.

The purpose of this paper is to design a robust control scheme to achieve robust tracking of velocity and altitude commands for a general hypersonic vehicle (HSV) in the presence of parameter variations and external disturbances.

The robust control scheme is composed of nonsingular terminal sliding mode control (NTSMC), super twisting control algorithm (STC) and recurrent neural network (RNN). First, by combing a novel NTSMC and STC algorithm, a second order NTSMC approach for HSV is proposed to provide fast, continuous and high precision tracking control. Second to relax the requirements for the bounds of the lumped uncertainties in control design, a RNN disturbance observer is presented to increase the robustness of the control system. The weights of RNN are updated by adaptive laws based on Lyapunov theorem, thus the closed‐loop stability can be guaranteed.

Simulation results demonstrate that the proposed method is effective, leading to promising performance.

The main contributions of this work are: first, both parameter variations and external disturbances are considered in control design for the longitudinal dynamic model of HSV; and second, the proposed controller can remove chattering and achieve more favorable tracking performances than conventional sliding mode control.

This paper aims to propose a reference model based simple strategy for the design of proportional-integral-derivative (PID) controller using frequency response matching for high-order stable, integrating and unstable processes that may have time-delay and non-minimum phase zero.

The reference sensitivity model is designed fulfilling stability conditions of the control system responses such as set-point response, load-disturbance response and noise response along with transient response criteria. The analytical controller thus designed is approximated to a PID controller using a simple formula based on a model-matching technique at low frequency.

PID controllers are designed for examples with varied dynamics taken from the literature, and the performances of the designed control systems are compared with some methods prevalent in the literature to show the efficacy of the proposed work. Overall, the method gives satisfactory set-point, as well as load-disturbance responses and controller-outputs in all the cases considered.

The method is applicable to high-order processes of various monotonic or oscillating dynamics without requiring process reduction. The PID controller designed considering a reference model with suitable criteria ensuring stability and a modified model matching technique, which provides a stable control system for all these high-order processes.

This paper aims to propose output feedback-based control algorithms for the flight control system of a scaled, un-crewed helicopter in its hover flight mode.

The proposed control schemes are based on H_∞ control and composite nonlinear control. The gains of the output feedback controllers are obtained as the solution of a set of linear matrix inequalities (LMIs).

In the proposed schemes, the finite-time convergence of system states to trim condition is achieved with minimum deviation from the steady-state. As the proposed composite nonlinear output feedback design improves the transient response, it is well suited for a scaled helicopter flight. The use of measured output vector instead of the state vector or its estimate for feedback provides a simple control structure and eliminates the need for an observer in real-time application. The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints.

The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints. They also have significance in applications where the number of measurement quantities needs to be minimized such as in a fully functional rotor-craft unmanned aerial vehicle.

The developed output feedback control algorithms can be used in small-scale helicopters for numerous civilian and military applications.

This work addresses the LMI-based formulation and solution of an output feedback controller for a hovering un-crewed helicopter. The stability and robustness of the closed-loop system are proved mathematically and the performance of the proposed schemes is compared with an existing strategy via simulation studies.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

The purpose of this research is to design a robust high-performance nonlinear multi-input multi-output heating, ventilation and air conditioning (HVAC) system controller for temperature and relative humidity regulation. Buildings are complex systems which are subjected to many unknown disturbances. Further complicating the control problem is the fact that, in practice, buildings and their systems have static nonlinearities such as power saturation that make stability difficult to guarantee. Therefore, in order to overcome these issues, a control system must be designed to be robust (performance insensitive) against uncertainties, static nonlinearities and effectively respond to unknown heat load and moisture disturbances.

A state of the art nonlinear inverse dynamics (NID) technique is combined with a genetic algorithm (GA) optimisation scheme in order to improve robustness against uncertainty in the system's modelling assumptions. The parameter uncertainty problem is addressed by optimising the control system parameters over a specified range of uncertainty. The NID control structure provides further robustness with effective disturbance handling and a stability criteria that holds in the presence of actuator saturation.

The proposed method delivers significantly more energy efficient performance whilst achieving improved thermal comfort when compared with a current industry standard HVAC controller design such as proportional-integral-derivative. The expected excellent response to disturbances is also demonstrated.

This method can easily be extended to account for other parameters with a specified uncertainty range.

This research presents a method of optimised NID controller design which can be easily implemented in real HVAC controllers of building energy management systems with a high degree of confidence to provide high levels of thermal comfort whilst significantly reducing energy usage.

A novel HVAC optimised NID control strategy using the robust inverse dynamics estimation feedback control topology with GA optimisation for improved robustness and tuning over a range of parameter uncertainty is described, designed and its performance benefits shown through simulation studies.

The hybrid control system of the nonlinear PID (NLPID) controller and improved active disturbance rejection control (IADRC) are proposed for stabilization purposes for a 6-degree freedom (DoF) quadrotor system with the existence of exogenous disturbances and system uncertainties.

IADRC units are designed for the altitude and attitude systems, while NLPID controllers are designed for the x−y position system on the quadrotor nonlinear model. The proposed controlling scheme is implemented using MATLAB/Simulink environment and is compared with the traditional PID controller and NLPID controller.

Different tests have been done, such as step reference tracking, hovering mode, trajectory tracking, exogenous disturbances and system uncertainties. The simulation results showed the demonstrated performance and stability gained by using the proposed scheme as compared with the other two controllers, even when the system was exposed to different disturbances and uncertainties.

The study proposes an NLPID-IADRC scheme to stabilize the motion of the quadrotor system while tracking a specified trajectory in the presence of exogenous disturbances and parameter uncertainties. The proposed multi-objective Output Performance Index (OPI) was used to obtain the optimum integrated time of the absolute error for each subsystem, UAV quadrotor system energy consumption and for minimizing the chattering phenomenon by adding the integrated time absolute of the control signals.