Virtual Control Signals Research Articles

NUMERICAL METHOD FOR CONTROLLING THE DYNAMICS OF ROTATION MOTION OF A MULTIROTOR UNMANNED AERIAL VEHICLE

There is a numerical method presented that makes it possible to ensure the smoothness of the forming function defining the rotational motion of a multirotor unmanned aerial vehicle. Behavior of the generating function at the final integration step is investigated. The proposed method provides the formation of a rotational motion limited by the value of angular velocity with specified angular acceleration parameters. The numerical method was simulated when forming a virtual control signal for back-stepping control. The results of simulating the response to a step signal showed a decrease in consumed energy, an effective limitation of angular velocity and a significant decrease in the peak power consumption compared to the original back-stepping method with a slight increase in the transition process time. The values of virtual control parameters which ensure the formation of specified dynamic characteristics of the rotational motion of multirotor unmanned aerial vehicle were selected.

IBLF-Based Finite-Time Adaptive Fuzzy Output-Feedback Control for Uncertain MIMO Nonlinear State-Constrained Systems

This article considers the problem of finite-time adaptive fuzzy output-feedback control design for multi-input–multioutput uncertain nonlinear systems subject to full state constraints. By employing the finite-time stability theory, a new finite-time adaptive fuzzy output-feedback control approach is proposed. An integral barrier Lyapunov functional is utilized to prevent all states from violating their constraints. Fuzzy logic systems are developed to approximate the uncertainties. A fuzzy state observer is constructed to estimate the unmeasurable states. Moreover, to handle the “explosion of complexity” issue in the backstepping control technique, a finite-time convergent differentiator is introduced to estimate the time derivatives of virtual control signals. The stability analysis showed that the control approach guarantees that all closed-loop signals are bounded, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed control scheme is confirmed by numerical simulations.

Indirect adaptive robust control of nonstrict feedback nonlinear systems by a fuzzy approximation strategy

To solve the control problem of nonstrict feedback nonlinear systems, a backstepping technique based controller design method is developed by integrating a robust control law with the excellent parameter identification algorithm of indirect adaptive framework. Each of unknown system functions that contain whole states is approximated by adding together a bounded time-varying parameter and a fuzzy approximator related to the current step and previous states only in the backstepping design procedure, which solves the algebraic loop problem existing in nonstrict feedback systems. Then the command filter is combined with the adaptive backstepping to construct the robust control law by which the differentiation operation of the virtual control signal can be avoided. Subsequently the swapping scheme is used to convert the studied system into a linear time-varying form. Both the weight vector of the fuzzy logic system and the bounded time-varying parameter are estimated by a least-squares identification algorithm under relaxed excitation conditions, so that the accurate value of system functions can be obtained. All signals in the closed-loop system are proved to be bounded. A simulation example is put forward to verify effectiveness of the presented method.

Command filtered finite‐time formation tracking control of AUVs with unknown control directions

This study investigates a formation control problem of autonomous underwater vehicles (AUVs) with unknown control directions. By employing the Nussbaum gain technique to deal with the unknown control directions, a finite-time formation control approach is proposed. To deal with the so-called ‘explosion of complexity’ issue in backstepping control, a compensator-based command filter technique is introduced to realise the derivative of virtual control signals. With the proposed method, the formation can be achieved within a finite settling time and all signals in the closed-loop system are uniformly bounded. The effectiveness and performance is demonstrated by numerical simulations.

Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis.

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.

Finite-Time Adaptive Fuzzy Tracking Control for a Class of Nonlinear Systems With Full-State Constraints

In this article, a new command filtered backstepping based finite-time adaptive fuzzy tracking control scheme for a class of unknown nonlinear systems with full-state constraints is established. First, the proposed finite-time command filter will filtering the virtual control signal and get the intermediate control signal within finite-time, so the problem of calculating complexity will not occur in the backstepping process. Then, the fraction-power-based error compensate signal is set up, which can eliminate the influence of filtering error on the control performance. Considering that the unknown nonlinearities exist in the system, the fuzzy logic system based adaptive control technique is used to deal with them, and only one parameter needs to be estimated. It is shown that the states will not violate the prescribed constrains, all the signals in the closed-loop system are bounded in finite-time and the tracking error can converge to the desired neighborhood of the origin in finite time under the barrier Lyapunov function and fraction-power-based virtual control signals. Finally, the effectiveness of the control method is shown by the simulations.

Robust extended state observer-based three dimensional integrated guidance and control design for interceptors with impact angle and input saturation constraints

In this paper, a new three-dimensional integrated guidance and control (IGC) scheme, considering impact angle and actuator saturation constraints, is presented for a skid-to-turn interceptor. The proposed method is based on backstepping, command filter, sliding mode control (SMC), and super-twisting extended state observer (STESO). To this end, a six-degrees-of-freedom realistic model, including errors between line-of-sight and interceptor velocity angles, model uncertainties, external disturbances, and limits for actuators, is constructed. Command filters effectively solve the explosion of complexity related to the derivative of virtual control signals in the backstepping method, and saturation limits for actuators. A combination of SMC and finite-time STESO guarantees strong robustness of the proposed IGC against lumped disturbances and target accelerations. System stability and uniformly ultimately boundedness of all states are proven using the Lyapunov stability theory. Finally, extensive numerical simulations are conducted to illustrate the effectiveness of the proposed IGC method.

About Realizations of Compatible Control Impacts on the Object in the Man—Machine Systems

We consider the problems of implementing control actions on the object in the ergatic system man—machine, combined through the control body of the human-machine interface apparatus, in order to effectively distribute the control functions between the human operator and the system’s control automat. Peculiarities of technical realization of operating influences on object ergatic system using the apparatus combines new type of control, through a management body of which is communication between a human operator and automatic system. The principle of construction of joint control devices is shown with the help of pictorial models of discrete and continuous operation devices that have an Electromechanical drive device that reacts to signals from the control machine due to the movement of the control body. The devices provide for the possibility of moving the control body and the muscular effort of the human operator. For the verbal model of actions and responses of the human operator and the machine, adopted in engineering psychology, mathematical models of joint control of the object of the ergatic system, describing the movements of the control body, are developed. The mathematical model of the response of the controlled object of the system is represented by a normal system of ordinary differential equations. As a result of composition of models of actions and reactions for the system man—machine obtained a finite set of incomplete representations of the elementary movements depicting point in the state space, which is constructed for controlled movement of an object in a sequence of elementary movements. Virtual discrete control signals corresponding to specific positions of the control bodies of the joint control devices of the human-machine interface of the system, which are used for sequential transitions from one elementary movement to another, are determined. The results of approbation of joint traffic control of vessels using a mathematical model of actions and responses in full-scale experiments with increased requirements for safety and accuracy of movement are presented. Conclusions are formulated about the effectiveness of the use of Sov-local management in transport ergatic systems man—machine. It is shown that in this case, the advantages of management partners are most used and combined.

Tracking control of a class of nonlinear systems with output delay based on adaptive fuzzy dynamic surface control

ABSTRACTThis paper concerns with the tracking control problem of a class of unknown nonlinear time-delay systems in the presence of output delay and inaccessible states. To deal with the difficulties arising from unmeasurable states, a fuzzy logic observer based on delayed outputs is firstly employed. Then, based on the delayed output, observed states and filtered virtual control signals, proper change of coordinates is defined for the purpose of designing dynamic surface control. By introducing appropriate Lyapunov-Krasovskii functionals in the design procedures, a controller and adaptation laws are obtained for the control strategy of nonlinear systems subject to output delay. Moreover, by selecting design parameters and matrices based on the acquired stability criteria, it is proved that the proposed adaptive fuzzy dynamic surface output feedback control approach is able to guarantee the uniformly ultimately boundedness of all the signals in the closed loop unknown nonlinear output-delay systems. Finally, the theoretic achievements are applied to three well-known benchmark systems to illustrate the effectiveness and efficiency of the proposed approach.

Prescribed Performance Tracking Control of a Class of Uncertain Pure-Feedback Nonlinear Systems With Input Saturation

In this paper, we address the issue of the prescribed performance control of a class of pure-feedback nonlinear systems with uncertainties and input saturation. The active disturbance rejection control is adopted at each step of the backstepping technology. In detail, the extended state observer is employed to estimate unknown functions to compensate for uncertain items. The tracking differentiator is used to take place of the derivative of the virtual control signals, which overcomes the repeated differentiations of nonlinear functions. The control input limitation is handled with the auxiliary system, and predefined tracking performance functions are utilized to improve the tracking accuracy and speed. By means of the input-to-state stability and Lyapunov stability theory, the tracking error is proven to converge to arbitrarily small neighborhood of the origin. Two simulation examples are provided to demonstrate the effectiveness of the presented results.

Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered Backstepping.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal. In addition, the input saturation problem is dealt with by introducing the auxiliary system. Overall, it is shown that the designed controller drives the output tracking error to the desired neighborhood of the origin at a finite time and all the signals in the closed-loop system are bounded at a finite time. Two simulation examples are given to demonstrate the control effectiveness.

Adaptive Finite-Time Containment Control of Uncertain Multiple Manipulator Systems.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering errors caused by the used FTCFs are removed by using the error compensation mechanism (ECM). The proposed virtual control signal, the control torque, and the adaptive updating law can guarantee the set tracking errors converge to an adjustable neighborhood of the origin in finite time in the presence of uncertain parameters. Because the virtual control signal and ECM only use the local information, the established method is completely distributed. Two simulation examples are given to show the effectiveness of the proposed scheme.

Four-Wheel Independent Drive Electric Vehicle Stability Control Using Novel Adaptive Sliding Mode Control

Four-wheel independent drive is an important research topic for the future development of pure electric vehicles (EVs) due to its fast response and great flexibility of path tracking. One of the critical issues is to maintain the vehicle motion stability of the four-wheel independent drive (4WID) EV (4WID EV) by appropriately distributing the torque of each wheel. The article presents a hierarchical control structure to counteract the stability problems caused by the actuator faults and road friction uncertainties. Instead of using linear control at the top-level (i.e., the outer control loop), a novel sliding mode controller with adaptive proportional-integral (PI) sliding surface is designed to achieve the required virtual control signal. For the bottom-level controller (or the inner loop controller), dynamic control allocation is employed to optimally allocate the required actual torque for each wheel. The stability of the overall closed-loop system is successfully achieved and proved by using the Lyapunov method. The proposed controller has been tested via simulation and experiments with various actuator faults and road conditions. All the test cases showed the superiority of the proposed method compared to some of the currently existing 4WID control strategies.

Neuro-adaptive trajectory tracking control of underactuated autonomous surface vehicles with high-gain observer

This paper addresses the trajectory tracking control of underactuated autonomous surface vehicles subject to immeasurable velocities and parameter uncertainties. An adaptive control scheme is proposed based on backstepping method, neural network and low-frequency techniques. In addition, the overall signals of closed-loop system are ensured uniformly ultimate boundness based on Lyapunov stability theory. The advantages are highlighted as follows: (i) To avoid explosion of complexity of conventional backstepping method, the derivations of virtual control signals are obtained through second-order tracking differentiator. (ii) The proposed controller does not require any previous knowledge about hydrodynamic damping and external disturbances, which is easily applied into practice. (iii) To solve the problem of immeasurable velocities, the controller does not acquire velocity signals by employing high-gain observer. Finally, simulation results are provided to verify strong robustness and tracking effectiveness of proposed control scheme

Neural network adaptive position tracking control of underactuated autonomous surface vehicle

The present study investigates the position tracking control of the underactuated autonomous surface vehicle, which is subjected to parameters uncertainties and external disturbances. In this regard, the backstepping method, neural network, dynamic surface control and the sliding mode method are employed to design an adaptive robust controller. Moreover, a Lyapunov synthesis is utilized to verify the stability of the closed-loop control system. Following innovations are highlighted in this study: (i) The derivatives of the virtual control signals are obtained through the dynamic surface control, which overcomes the computational complexities of the conventional backstepping method. (ii) The designed controller can be easily applied in practical applications with no requirement to employ the neural network and state predictors to obtain model parameters. (iii) The prediction errors are combined with position tracking errors to construct the neural network updating laws, which improves the adaptation and the tracking performance. The simulation results demonstrate the effectiveness of the proposed position tracking controller.

Supervisory switching‐based prescribed performance control of unknown nonlinear systems against actuator failures

SummaryThis article studies the fault‐tolerant control problem for unknown nonlinear strict‐feedback systems subject to actuator failures yet with dynamic redundancies. The prescribed performance control methodology is newly combined with a modification‐based supervisory switching strategy to solve the problem. To implement failure detection, the performance function is properly modified to synthesize a monitoring function to supervise the behavior of an error variable. Once a failure is detected, the current actuator is shut down and the backup actuator is switched in to execute the reconfigured control command. Compared with the existing results, (1) the postfailure and postswitching tracking performance is improved, other than uniform ultimate boundedness and (2) the dependence on extra robust control schemes (eg, adaptive or approximating structures) to deal with model uncertainties or the need to compute analytic derivatives of virtual control signals in the backstepping design is eliminated.

Adaptive Dynamic Surface Control for Active Suspension With Electro-Hydraulic Actuator Parameter Uncertainty and External Disturbance

In this paper, a new adaptive dynamic surface control strategy is proposed to deal with the uncertainty parameters of electro-hydraulic actuator and unknown external disturbances of vehicle active suspension system. A dynamic model of nonlinear vehicle active suspension is established. The adaptive parameter estimation laws are designed to estimate the uncertain parameters caused by the change of the physical characteristics of the electro-hydraulic actuator. In other words, the estimation of uncertain parameters in hydraulic system is beneficial to improve the control precision of vehicle. Nonlinear robust feedback signal is introduced to suppress unknown external disturbances. Then, this paper proposes an idea of dynamic surface in order to avoid explosion of complexity that caused by virtual control signal. The dynamic surface function replaces the derivative of virtual control signal in backstepping control, which reduces the computational complexity and improves the riding comfort. The simulation comparison shows that the presented method is effective in improving riding comfort and driving safety.

Improved Adaptive Dynamic Surface Control for a Class of Uncertain Nonlinear Systems

An improved dynamic surface control (IDSC) approach is presented for a class of strict-feedback nonlinear systems with unknown functions. The proposed method makes the state errors get rid of the influence of first-order filters, which simplifies the design of control. By employing neural networks to account for system uncertainties, the virtual control signal of the IDSC is directly used to construct the state error instead of the signal generated by the first-order filter in the dynamic surface control (DSC) method. The stability of the method is proved by Lyapunov stability theory, and the semi-global uniform ultimate boundedness of all signals in the closed-loop system is guaranteed. Simulation results demonstrate the IDSC method has better tracking performance and stability than traditional DSC method.

Backstepping Control of Heavy Lift Operations with Crane Vessels

Offshore structures with large mass are installed and removed by heavy lift vessels. During offshore constructions, two safety-critical interconnected operations take place, the dynamic positioning of the vessel and the lifting of the heavy structure by an immovable boom crane on the vessel. Existing studies on offshore boom crane control either neglect the structure (load) dynamics in sway and the vessel movement, or consider the boom angle of the crane controllable. In this paper, we present a control scheme for underactuated offshore structure, taking into consideration the impact of the dynamic positioning of the vessel on the physical load model. The proposed control scheme is designed following a backstepping control approach using command filtering to generate virtual control signals and their derivatives avoiding the analytic differentiation. Simulation results are obtained by applying the control scheme in a dynamic positioned vessel-load model showing that the controller is able to stablize the load position during the vessel dynamic positioning.

High-gain-observer based adaptive output-feedback formation control for underactuated unmanned surface vessels with input saturation and uncertainties

An adaptive output feedback formation control strategy based on high gain observer has been developed to solve the problem of underactuated surface vessels formation control with uncertain dynamics, ocean environment disturbance and input saturation. In this strategy, a high gain observer that only depends on position information is used to estimate the unmeasurable velocity, and in order to solve the ‘complex explosion problem’ in the conventional backstepping control algorithm, a first-order low-pass filter is adopted to obtain the derivative of the virtual control signal. In addition, adaptive neural networks (NNs) and minimum learning parameters (MLPs) algorithm are used to approximate environmental disturbances and uncertain dynamics, while reducing online update parameters. Stability analysis proved that all signals in closed-loop are uniformly ultimately bounded and the formation tracking errors are arbitrarily small. Simulation results demonstrate the effectiveness of the controller.