Command Filter Research Articles

Finite-Time Command-Filtered Lateral-Longitudinal Formation Control of Vehicles on Rough Roads

This paper investigates the lateral and longitudinal formation control problem of a group of vehicles driving in rough road environments. An adaptive sliding mode disturbance observer is introduced to estimate the road disturbance. Then, based on the road disturbance observer, a conmand-filtered vehicular formation control method is given, which can make the formation tracking errors converge to a small neighborhood of zero in a finite time. In other words, the lateral-longitudinal formation of vehicles can be practically finite-time stabilized despite the road disturbance, via the use of a backstepping control algorithm with a finite-time command filter based on an error compensation mechanism. Numerical simulations have shown the effectiveness of the presented method.

An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.

Event-triggered finite-time fuzzy control approach for fractional-order nonlinear chaotic systems with input delay

In this paper, a novel fuzzy event-triggered control approach with guaranteed performance of practical finite-time tracking convergence is constructed for uncertain fractional-order nonlinear chaotic systems in the presence of time-varying input delay. Firstly, fuzzy logic systems are employed to deal with immeasurable systematic information. Secondly, fractional-order command filters are incorporated to circumvent the problem of “complexity explosion” in backstepping controller construction. Meanwhile, a sequence of fractional-order finite-time compensation signals are introduced to counteract the adverse influence of time-varying input delay rapidly. Furthermore, a novel practical finite-time chaos control approach based on event-triggered strategy is proposed, which not only facilitates the decrease of sampling frequency and controller burden dynamically, but also ensures the convergence of all tracking errors towards a small region around the origin in finite time. Finally, through the simulation research, the transient response performance and the chaos-suppression ability of fractional-order chaotic systems stabilized via the suggested approach are rigorously testified.

ADAPTIVE NEURAL TRACKING CONTROL FOR SURFACE SHIP WITH COMPENSATED TRACKING ERROR-CONSTRAINS AND DELAY INPUT BASED ON COMMAND-FILTER

The article proposes an algorithm for trajectory tracking control problem of full actuated surface ships in the presence of state constraints, the delay of the input signal, and uncertain model parameters. During the design process, radial basis function neural networks are used to approximate the nonlinear components of uncertainty and a symmetric barrier Lyapunov function is incorporated to cope with the constraints of compensated tracking error. In particular, an auxiliary system is employed to eliminate the delay of the input signals, which often makes the control performance worse, even unstable. The adaptive controller that the article proposes is built based on the backstepping method using a command filter to avoid derivative explosion and reduce the computational burden on the controller. The article shows that tracking errors of surface ship can converge to a small neighborhood of zero, the compensated tracking error constraints of the system are not violated, the system is still stable when the input signal is delayed.

Switching ETM-based neural adaptive output feedback control for nonaffine stochastic MIMO nonlinear systems subject to deferred constraint

This article focuses on the neural adaptive output feedback control study related to nonaffine stochastic multiple-input, multiple-output nonlinear plants. First, a K-filter state observer based on a radial basis function neural network is designed to estimate the remaining unavailable states. Then, a novel adaptive command-filtered backstepping output feedback control framework is established, where an improved command filter with a fractional-order parameter is applied to conquer the calculation size problem. Specifically, the highlight of this work is that it designs a modified error compensation signal and incorporates the concept of deferred constraint to eradicate the negative effect caused by the filter errors. In addition, the network bandwidth resources, control impulse, and control accuracy are synthesized using an amended switching event-triggered mechanism. The theoretical analysis proved that the proposed control approach guarantees that the tracking error can converge to a preassigned region within a user-defined time while the violation of the deferred output constraint can be excluded. Two illustrative studies are provided to demonstrate the validity and superiority of the developed control method.

Time‐varying state constrained estimators‐based command filtered quantized consensus control for nonlinear multiagent systems

AbstractThis article investigates the command filtered backstepping quantized consensus control for the time‐varying full‐state constrained nonlinear multiagent systems based on unknown system dynamics estimators (USDE). First, different from the intelligent control and the USDE‐based control methods, a novel time‐varying state constrained (TVSC)‐USDE is developed to estimate the unknown nonlinear dynamics and disturbances exactly with fewer parameters, where the states in TVSC‐USDE satisfy the state constraints requirements. The proposed TVSC‐USDE can reduce the computational burdens that caused by the online estimation of weights. Second, distinguish from current state constraints methods, by designing the integral barrier Lyapunov functions (IBLFs), the conservatism feasibility conditions are removed, which is more reasonable for the physical multiagent systems. Furthermore, the explosion of complexity (EOC) is a vital issue to tackle under the IBLFs backstepping control schemes. For this purpose, this article designs the command filters to gain the derivatives and handle the EOC problem. Further, to counterbalance the filtering errors, the error compensation signals are first developed in IBLFs‐based control method. Finally, the input quantized signals are introduced for the limited bandwidth problem in multiagent network systems, and the proposed control approach can attain consensus tracking under low communication rates and small tracking errors.

Novel Adaptive Event-Triggered Fuzzy Command Filter Control for Slowly Switched Nonlinear Systems With Constraints.

This article addresses the adaptive fuzzy control problem for switched nonlinear systems with state constraints. The unified barrier function (UBF) is introduced to solve the time-varying state constraints, which removes the feasibility conditions. By integrating command filter into backstepping control to avoid the "explosion of complexity." In addition, a novel event-triggered strategy is designed to deal with the asynchronous switching between subsystems and controllers without limiting the maximum asynchronous time, and mitigate the communication burden. Also, a new threshold function is introduced to overcome the difficulty of discontinuous triggering error at the switching instants. Then, by combining the improved admissible edge-dependent average dwell-time (AED-ADT) method with Lyapunov stability analysis, it is proved that all system signals are bounded and do not violate the predefined constraints under given switching rule. Finally, the numerical simulation results verify the superiority of the proposed algorithm, and the algorithm is applied to a ship maneuvering system.

Distributed Adaptive Gain-Varying Finite-Time Event-Triggered Control for Multiple Robot Manipulators With Disturbances

This paper investigates the consensus tracking of uncertain multiple robot manipulators with disturbances, in which a distributed adaptive gain-varying finite-time event-triggered strategy is given. To build the connections between error information and control gains, the well-designed dynamic gain functions are added to the static gains, then the anti-disturbance ability of the networked system is strengthened when there are strong disturbances. The command filters are employed to avoid the direct differentials of virtual controllers and the compensation strategy with dynamic gains removes the filtering errors compared with traditional backstepping-based algorithms. The event-triggered mechanism is further used to reduce the waste of communication resources by avoiding frequent controller updating for each manipulator in the complex system. Three simulation examples are used to demonstrate its effectiveness.

Adaptive Iterative Learning Control for a Class of Nonlinear Strict-Feedback Systems With Unknown State Delays.

In this article, an adaptive iterative learning control scheme is presented for a class of nonlinear parametric strict-feedback systems with unknown state delays, aiming to achieve the point-wise tracking of desired trajectory in a finite interval. The appropriate Lyapunov-Krasovskii functions are established to compensate the influence of time-delay uncertainties on the control systems. As the main features, the proposed approach integrates the command filter into the backstepping procedure to avoid the differential explosion problem that may occur with the increase of system order, and introduces the hyperbolic tangent functions into the learning controller to handle the singularity problem thus maintaining the continuity of input signal. The results of theoretical analysis and numerical simulation demonstrate that the tracking errors at the entire period will converge to a compact set along the iteration axis. Compared with the existing works, the proposed control scheme is promising to manifest the better performance and practicability owing to the learning mechanism, the dynamic model, as well as the implementation of controller.

Adaptive neural practical fixed‐time command filtered control for multi‐input and multi‐output nonlinear systems with dead zones input and unknown control direction

AbstractIn this article, under the circumstance of dead zones input and unknown control direction, the adaptive practical fixed‐time control strategy is presented for a general class of multi‐input and multi‐output (MIMO) nonlinear systems. The inherent explosion of computational complexity difficulty is eliminated by adopting a command filter technique and the universal approximation properties of radial basis function neural networks (RBFNNs) are applied to model the unknown nonlinear functions. The difficulties of the dynamic surface method and unknown directions can be handled by invoking error compensation mechanism and Nussbaum‐type functions, respectively. The uniqueness of the presented control scheme is that the tracking system can achieve the fixed‐time stability without relying on the boundedness of dead‐zone parameters. The fixed‐time convergence of the output tracking error and the semiglobally fixed‐time stable of closed‐loop system are assured via the developed adaptive fixed‐time command filtered controller. Finally, a practical example is supplied to further validate the availability of the presented theoretic result.

Command Filter AILC for Finite Time Accurate Tracking of Aircraft Track Angle System Based on Fuzzy Logic

In this paper, the longitudinal model of an uncertain aircraft is taken as the research object, and the aircraft path inclination is controlled by controlling the input rudder deflection angle. An adaptive iterative learning control (AILC) scheme is proposed to solve the accurate tracking control problem of the flight path inclination on a finite time interval. The aircraft track angle system is abstractly modeled to obtain a triangular model in the form of strict feedback. For the abstracted strict feedback model, the fuzzy logic is used to approximate the uncertain part of the model. A command filter and an error compensation mechanism are introduced to prevent the computational bloat problem caused by excessive system order, and a convergent series sequence is used to deal with the truncation error caused by the approximation of the fuzzy logic. Based on the Lyapunov stability theorem, all signals of the closed-loop system are bounded on the finite time interval 0 , T , and the output of the system can track the desired trajectory accurately. Finally, the feasibility and effectiveness of the method are verified by MATLAB simulation results.

Event-trigger-based composite adaptive fuzzy control for nonlinear time-varying state constraint systems with asymmetric input saturation

In this paper, a novel composite adaptive fuzzy control (AFC) based on the command filtered backstepping and event-triggered mechanism is proposed for nonstrict-feedback nonlinear systems with input saturation and time-varying full-state constraints. A fuzzy observer is designed to estimate the unmeasured states. Simultaneously, by introducing a serial-parallel estimation model, a fuzzy control tactic with the composite adaptive law of parameters is established which greatly increases the precision of unknown term estimation. In addition, the command filtering technique is employed to solve the issue of “explosion of complexity” and compensate for the errors caused by the command filters. The event-triggered mechanism is utilized to effectively decrease the data redundancy. By using the time-varying barrier Lyapunov functions (BLFs), all the states can remain within a prescribed time-varying interval. The designed controller can guarantee the convergence of the tracking error at the origin and the boundedness of all closed-loop signals. Finally, two simulation examples are carried out to validate the effectiveness of the proposed control scheme.

Time-Varying BLFs-Based Adaptive Neural Network Finite-Time Command-Filtered Control for Nonlinear Systems

This article deals with the adaptive neural network (NN) finite-time (FT) command-filtered tracking control problem for a class of nonlinear systems with time-varying full-state constraints. Based on the asymmetric time-varying barrier Lyapunov functions (TVBLFs), the issue of time-varying full-state constraints is settled. The influence of unknown items in the system can be eliminated by the adaptive NN control method. Moreover, the improved FT command filter is introduced to relax the restriction on the input signal and solve the explosion of complexity (EOC) problem. Meanwhile, the FT error compensation mechanism is developed to eliminate the influence of filtering error. It is shown that the proposed strategy can guarantee FT boundedness of all the signals in the closed-loop system and FT convergence of the tracking error. An example verifies the effectiveness of the proposed control method.

Adaptive backstepping fuzzy synchronization control of fractional-order chaotic systems with input saturation and external disturbances

In this paper, an adaptive backstepping command filtered controller is proposed for a class of uncertain strict feedback fractional-order chaotic systems with input saturation and external disturbances. A command filter is designed to avoid the “explosion of complexity” problem in the conventional backstepping technique. To tackle with filter error and improve synchronization accuracy, a compensation mechanism is provided. Meanwhile, fuzzy logic systems are utilized to approximate unknown functions, and disturbance observers are constructed to reduce the impact of unknown disturbances. In particular, to reduce the chattering phenomenon, a smooth function rather than the sign function is used in the controller design, and the stability of the closed-loop system can be guaranteed by the proposed synchronization controller. A simulation study is provided to confirm the practicality and validity of the proposed method.

Fuzzy-Model-Based Dynamic Event-Triggered Control in Sensor-to-Controller Channel for Nonlinear Strict-Feedback System via Command Filter

This paper considers the situation of sensors transmit plant states to controller in a dynamic event-triggered manner and develops a fuzzy-model-based adaptive command filtered tracking control method for nonlinear strict-feedback systems with uncertain dynamics. Firstly, the dynamic event-trigger rules are designed and implemented in sensor-to-controller channel to reduce communication frequency in network controlled system. Then, the adaptive fuzzy-model is designed to generate approximated states for controller during the event-trigger intervals to avoid an open-loop like system operation which can be caused by the traditional zero-order-hold policy. Moreover, command filter technique is incorporated to solve the issue of “jumping of virtual controller” and circumvent “explosion of complexity” problem during the fuzzy-model-based event-triggered controller design process. Meanwhile, the filtering errors are eliminated by compensate signals. Finally, two simulation examples are conducted and the results show the effectiveness and superiority of the proposed method.

Fixed-Time Distributed Adaptive Formation Control for Multiple QUAVs With Full-State Constraints

This paper devises a fixed-time distributed adaptive formation control algorithm under the event-triggered framework to guarantee the expected formation pattern for multiple quadrotor unmanned aerial vehicles (QUAVs) with full-state constraints. The multiple QUAVs subject to full-state constraints are transformed into the ones that are free from any constraints via a time-varying nonlinear transformation function (NTF), which is effective to handle the case regardless of whether there exist state constraints. The issue of “explosion of complexity” (EOC) as well as singularity problem are fully coped via the fixed-time command filter and the smooth switch function, respectively. To further improve the control performance of multiple QUAVs, the nonsmooth error compensation mechanism is constructed to compensate the filtered error resulting from command filter. The rigorous stability analysis of the developed event-trigger-based distributed formation control scheme proves that all signals of the closed-loop system are fixed-time bounded, and the states of multiple QUAVs will not violate the prescribed constraints and the formation tracking errors converge to a small region around the origin in a fixed time. Finally, simulation examples are performed to delineate the validity of the proposed distributed formation control algorithm.

Composite observer-based backstepping tracking control of fractional-order chaotic systems

An observer-based backstepping strategy is constructed for fractional-order chaotic systems. A disturbance observer and a state observer are simultaneously constructed to gain the estimations of uncertain disturbances and unmeasurable states, respectively, and an auxiliary system is developed to connect the two observers. Then, a fractional-order command filter is used to avoid tedious derivatives of virtual signals so as to reduce computational burden. A coupling backstepping controller, which ensures the convergence of tracking error and the boundedness of closed-loop signals, is constructed. Finally, two simulation examples are given to verify the effectiveness of theoretical analysis.

Command filtered backstepping control of constrained flexible joint robotic manipulator

AbstractHere, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method.

Adaptive finite time command filtered backstepping control design for a class of uncertain full state constrained strict‐feedback nonlinear systems

AbstractThis article investigates an adaptive finite time command filtered backstepping control design for a class of uncertain full state constrained strict‐feedback nonlinear systems. The proposed adaptive control strategy is constructed by combining the barrier Lyapunov functions (BLFs), the adaptive command filtered backstepping control method and the finite time control theory. The BLFs are utilized to overcome the violation of the full state constraints. The proposed adaptive control approach is introduced to avoid the problem of the explosion of complexity and the compensating signals are designed to eliminate the influence of the filtering errors caused by the command filters. Moreover, based on the Lyapunov stability theory, it is proved that all the signals of the closed‐loop system are practical finite time stable and the full state constraints are not violated. To verify the efficiency of the proposed adaptive control scheme, simulation results are provided for electromechanical system.

Adaptive fuzzy finite‐time formation asymptotic tracking control for nonholonomic multirobot systems with inputs quantization

SummaryThis paper investigates the finite‐time formation tracking control problem for nonholonomic multirobot systems with inputs quantization. The fuzzy logic systems (FLSs) and adaptive method are introduced to approximate unknown nonlinear functions and parameters. Multirobot needs to keep safe and effective communicating range, the barrier function and prescribed performance method are employed to ensure collision avoidance and connectivity maintenance. Then, in order to make each robot more precisely and quickly track referenced leader's trajectory and consider the finite channel bandwidth in practical applications, the travel range of robots can be maintained at more secure region and formation can operate normally within the given bandwidth. By combining the command filter and finite‐time theory, a novel decentralized finite‐time formation asymptotic tracking (FTFAT) control strategy is further proposed based on inputs quantization technology, it guarantees that all signals are bounded and the formation tracking errors asymptotically converge to zero in finite‐time. Finally, the practicability of FTFAT strategy is demonstrated absolutely for nonholonomic multirobot systems.