IET Control Theory and Applications最新文献

This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend-state-observer is utilized to estimate the velocity signals; then, a neural-network-based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural-network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade-off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two-degree-of-freedom robot manipulator.

Recent advances in concurrent learning based adaptive controllers have relaxed the persistency of excitation condition required to achieve exponential tracking and parameter estimation error convergence. This was made possible via the use of additional concurrent learning stacks in the parameter estimation algorithm. However, the proposed concurrent learning components, that is, the history stacks, needed to be filled with “selected” values dependent on the actual system states. Therefore, the previously proposed concurrent learning adaptive controllers required the system to be stable initially for a finite time so that the corresponding history stacks can be filled (finite excitation condition). In this work, motivated to remove the finite excitation condition, a novel desired system state based concurrent learning adaptive controller is proposed. In order to remove the system state dependencies in the controller and estimation algorithms, a filtered version of the dynamics and a novel prediction error formulation have been designed. The overall exponential stability, parameter error convergence and boundedness of the system states during closed loop operations are ensured via Lyapunov based arguments. The main advantages of the proposed method are its dependence on the desired system states and the overall stability results that paved the way in removing the need for finite excitation condition. Numerical studies performed on a two link robotic device are also presented to illustrate the feasibility of the proposed method.

This paper considers the secure distributed control consensus problem of linear multi-agent systems (MASs) under switching topologies, subject to intermittent sequential scaling attacks, which was compelled to scaling factor, attack frequency, duration and cooperative-competitive networks. First, the scenario of a fixed topology is considered, and a novel control protocol combined with a logarithmic quantizer and relative state measurements of neighbouring agents is discussed. The sighed graph is utilized to characterize the communication topology determined by the information flow directions and captured by the graph Laplacian matrix. After that, sufficient conditions for effectiveness of the developed control methods in guiding the MASs to secure bipartite leader-following consensus are constructed. Second, the scenario of switching topologies is considered, and it is derived that the secure bipartite consensus will be achieved if the designed state feedback control protocol with the scaling factor, the attack duration, attack frequency and switching signal are selected properly. At last, to prove the effectiveness of the designed controllers, a simulation example depended on the real-word actual military is introduced.

Herein, a control system and a fault tolerance method for the rotor positionable quadrotor are proposed. Quadrotors that have a variable structure are made for different purposes. The rotor-positionable quadrotor studied here, is a type of drone with a variable structure that has the ability to change the position of its rotors linearly along the axis of each arm. It can be seen that this capability can improve the drone robustness against disturbances and faults in comparison with regular quadcopters. Due to the over-actuated dynamics of this type of quadrotor, the control allocation scheme based on log-barrier optimization is employed to obtain the position and speed of each rotor. In this study, it is experimentally shown that rotor positioning not only reduces power consumption but also increases roll and pitch control inputs magnitude. Furthermore, when a fault occurs as a decrease in rotor speed, a fuzzy method is proposed to position the rotors which tolerates the fault. Finally, numerical simulations and experimental tests verified that rotor positioning can bring more robustness, reduction in power consumption, and fault tolerance in some rotor faults capabilities for quadrotors.

This paper proposes a novel solution to the problem of robust ripple-free hybrid robust regulator ensuring the elimination of intersample ripples. The paper considers a structurally stable regulation approach in the hybrid framework. A regulator is designed using this technique under periodic sampling and is applied to the nonlinear model of a quadrotor. The control of unmanned autonomous vehicles has become a research topic that is important by itself, particularly in the case of multi-rotor systems due to their versatility in various applications. Tracking a reference signal for such vehicles can be challenging, particularly when the frequency of communication is limited, or the control station is located far away. The proposed controller is hence tested for this challenging application.

This paper presents an analytical method to solve the optimal control problem for affine nonlinear systems with unknown drift dynamics. A new non-quadratic cost function over an infinite horizon is presented that considers input constraints and includes the cost of the feed-forward component of the control law. The mean value theorem for vector-valued functions has been used to derive an integral form of this theorem. Based on this theorem, a rigorous proof is provided demonstrating that the cost function can be converted into another form. In the presence of input constraints, this converted form enables extracting the optimal control solution without solving the HJB equation. Additionally, unknown nonlinearity effects in drift dynamics are compensated in the control input. This is accomplished by estimating the unknown drift dynamics via an adaptive neural network (NN) approach. It is proven that the states and weights of NN are uniformly ultimately bounded based on a Lyapunov technique. The necessary and sufficient conditions are provided that ensure the optimality of the infinite horizon optimal control problem with a discount factor. As a result, it is demonstrated that the proposed approach satisfies the optimality criteria. To evaluate the effectiveness of the proposed approach, simulation examples are provided.

This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co-axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed-loop system. Moreover, the closed-loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system.

This study focuses on the precise model estimation for a position control problem actuated by a shape memory alloy (SMA) wire. Because the hysteresis characteristic of SMA introduces complexities in system modelling and adds degrees of freedom, a model with reduced order is implemented for controller design. This model is online updated by calculating a complementary term from the measured data to compensate for the SMA actuator dynamics and other parametric uncertainties. The position controller, derived from the formulated reduced-order model, adapts itself to real conditions and is cost-effective due to the use of only displacement sensor. The saturation of the control input is modelled within the structure of a constrained optimization problem solved by Karush–Kuhn–Tucker theorem. The boundedness of mean and covariance of tracking error and its derivative is demonstrated by stochastic analysis. The experimental results conducted on a platform incorporating a SMA wire show the efficiency of the proposed system in precisely controlling the position by admissible voltage range. The comparative results with a sliding mode controller indicate higher accuracy for the proposed controller to reduce the effect of uncertainties.

This paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis-based QMPC (SA-QMPC) algorithm. It is proved that the computational complexity of SA-QMPC is $��O�\left(�N�n^2�\right)�$ for a region of the initial conditions and $��O�\left(�N^2�n^2�\right)�$ for sufficiently small sampling time and all initial conditions, where $�N$ and $�n$ are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm.

This paper studies the H_∞ control problem for polynomial time-varying systems. The H_∞ control problem has been much less investigated for time-varying systems in comparison to the time-invariant systems. Approximate dynamic programming (ADP) is an optimal method to solve the control problems. Therefore, it is valuable to solve the polynomial time-varying H_∞ control problem with the ADP approach. Considering the time as an independent variable for sum-of-squares (SOS) optimization problems, an SOS-based ADP method is proposed to solve this problem. A policy iteration algorithm is presented, where in its policy evaluation step it is sufficient to solve an optimization problem. Some constraints are added to this optimization problem to guarantee the closed-loop exponential stability. The convergence and stability properties of the proposed algorithm are stated and proven. Moreover, in order to design an H_∞ controller with a smaller disturbance attenuation coefficient, a two-loop algorithm is suggested. Finally, the effectiveness of the proposed method is demonstrated by simulation examples.