Showing papers in "Journal of The Franklin Institute-engineering and Applied Mathematics in 2017"
TL;DR: This paper presents a novel method to address a Proportional Integral observer design for the actuator and sensor faults estimation based on Takagi–Sugeno fuzzy model with unmeasurable premise variables by solving the proposed conditions under Linear Matrix Inequalities constraints.
Abstract: This paper presents a novel method to address a Proportional Integral observer design for the actuator and sensor faults estimation based on Takagi–Sugeno fuzzy model with unmeasurable premise variables. The faults are assumed as time-varying signals whose kth time derivatives are bounded. Using Lyapunov stability theory and L2 performance analysis, sufficient design conditions are developed for simultaneous estimation of states and time-varying actuator and sensor faults. The Proportional Integral observer gains are computed by solving the proposed conditions under Linear Matrix Inequalities constraints. A simulation example is provided to illustrate the effectiveness of the proposed approach.
205 citations
TL;DR: The proposed algorithm for formation of multiple linear second-order agents with collision avoidance and obstacle avoidance with recursive feasibility of the resulting optimization problem is guaranteed and closed-loop stability of the whole system is ensured.
Abstract: The paper is concerned with the problem of distributed model predictive control (DMPC) for formation of multiple linear second-order agents with collision avoidance and obstacle avoidance. All the agents are permitted to implement optimization simultaneously at each time step. The assumed input trajectory and state trajectory are introduced to obtain a computationally tractable optimization problem in a distributed manner. As a result, a compatibility constraint is required to ensure the consistency between each agent׳s real operation and its plan and to establish the agreement among agents. The terminal ingredients are tailored by making use of the specific form of the system model and the control objective. The terminal set is ensured to be positively invariant with the designed terminal controller. The collision avoidance constraint and the obstacle avoidance constraint are satisfied for any state in the terminal set. The weighted matrix of the terminal cost is determined by solving a Lyapunov equation. Moreover, recursive feasibility of the resulting optimization problem is guaranteed and closed-loop stability of the whole system is ensured. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.
153 citations
TL;DR: A decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering to transform the original system to a hierarchical identification model and to decompose this model into three subsystems and to identify each subsystem.
Abstract: This paper develops a decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering. The key is to apply the data filtering technique to transform the original system to a hierarchical identification model, and to decompose this model into three subsystems and to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm requires less computational efforts. The simulation results show that the proposed algorithms can work well.
148 citations
TL;DR: This paper extends the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection and proves its capacity to address portability across languages in translation tasks.
Abstract: Subjectivity detection is a task of natural language processing that aims to remove ‘factual’ or ‘neutral’ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks.
132 citations
TL;DR: Three novel concepts called closed-chain, l-open-chain and quasi-cyclic switching signals are introduced to derive some results that are not only simple but also less conservative in terms of linear matrix inequalities and can guarantee the global exponential stability of SPLSs under ADT switching.
Abstract: In this paper, stability analysis for switched positive linear systems (SPLSs) with average dwell time switching is revisited and discussed in both continuous-time and discrete-time contexts. By utilizing system positivity, two simple stability conditions with fewer constraints are first proposed for SPLSs on the basis of multiple linear copositive Lyapunov functions (MLCLFs). Based on the developed results, three novel concepts called closed-chain, l-open-chain and quasi-cyclic switching signals are introduced to derive some results that are not only simple but also less conservative. All the obtained results are formulated in terms of linear matrix inequalities and can guarantee the global exponential stability of SPLSs under ADT switching. To illustrate the advantages of our established results, a numerical example is finally given.
125 citations
TL;DR: It is proved that the MCUKF and MCUIF will converge to UKF and UIF, respectively, while existing MCU IF will not in this case and it generally has poor estimation accuracy as well.
Abstract: In this paper, we investigate the state estimation problem of nonlinear systems with non-Gaussian measurement noise. Based on a newly defined cost function which is obtained by a combination of weighted least square (WLS) and maximum correntropy criterion (MCC), we derive our maximum correntropy unscented Kalman filter (MCUKF) and the corresponding maximum correntropy unscented information filter (MCUIF). Comparing with existing MCUKF, our MCUKF avoids the numerical problem occurred when the measurements contain large outliers, and can obtain similar or even better estimation results. When the kernel bandwidth goes infinity, we prove that our MCUKF and MCUIF will converge to UKF and UIF, respectively, while existing MCUIF will not in this case and it generally has poor estimation accuracy as well. Two typical nonlinear models are used to illustrate the advantages of our proposed algorithms.
107 citations
TL;DR: The main purpose is to design a sampled-data controller so as to the synchronization error system (SES) is exponentially stable and satisfies a predefined H ∞ / passive performance index simultaneously.
Abstract: This paper investigates the problem of the mixed H ∞ / passive sampled-data synchronization control for complex dynamical networks (CDNs) with distributed coupling delay. The sampled interval is deemed as time-varying. The main purpose is to design a sampled-data controller so as to the synchronization error system (SES) is exponentially stable and satisfies a predefined H ∞ / passive performance index simultaneously. Some novel auxiliary function-based integral inequalities are applied to reduce the conservativeness of the presented results, and some effective synchronization criteria are addressed. The gains for the desired controller can be designed by settling an optimization issue in view of the proposed criteria. Three examples are employed to demonstrate the less conservativeness and superiority of the addressed method.
105 citations
TL;DR: An adaptive event-triggered communication scheme (AETCS) for a class of networked Takagi–Sugeno (T–S) fuzzy control systems is investigated, dependent on a novel adaptive law which can be achieved on-line rather than a predefined constant.
Abstract: This paper investigates an adaptive event-triggered communication scheme (AETCS) for a class of networked Takagi–Sugeno (T–S) fuzzy control systems. The threshold of event-triggering condition has great influence on the maximum allowable number of successive packet losses. Different from the conventional method, the threshold, in this study, is dependent on a novel adaptive law which can be achieved on-line rather than a predefined constant, since the threshold with fixed value is hard to suit the variation of the system. The stability and stabilization criteria are derived by using a new Lyapunov function. Finally, an example is provided to demonstrate the design method.
99 citations
TL;DR: The backstepping method is first adopted to solve the saturation problem of incommensurate fractional order systems with nonlinearities and uncertainties, which achieves stabilization and tracking.
Abstract: A fractional order adaptive backstepping control scheme is presented for an incommensurate fractional order systems in the presence of input saturation. In order to compensate the saturation, the necessary signals are generated by constructing a fractional order auxiliary system. Besides the nonlinear functions and unknown parameters of systems, the uncertainties, especially the unknown control input coefficient, are emphasized here. The adoption of fractional order parameters update law and nonlinear feedback has added more degree of freedom to controllers, which leads to a larger range of application. The frequency distributed model is introduced so that the indirect Lyapunov method is available in the procedure of controller design. All the differential signals, used for the control and estimation, are obtained through fractional order tracking differentiator. Compared with previous methods, the backstepping method is first adopted to solve the saturation problem of incommensurate order systems with nonlinearities and uncertainties, which achieves stabilization and tracking. To highlight the effectiveness of the proposed controller, simulation examples are demonstrated at last.
88 citations
TL;DR: A novel Lyapunov–Krasovskii functional is constructed with some new augmented terms, which can fully capture the system characteristics and the available information on the actual sampling pattern, and ensures the master–slave synchronization of CLSs under a longer sampling period than remarkable existing works.
Abstract: This paper investigates the problem of master–slave synchronization of chaotic Lur’e systems (CLSs) with time delays by sampled-data control. First, a novel Lyapunov–Krasovskii functional (LKF) is constructed with some new augmented terms, which can fully capture the system characteristics and the available information on the actual sampling pattern. In comparison with existing results, the constraint condition of the positive definition of the LKF is more relax, since it is positive definite only requiring at sampling instants. Second, based on the LKF, a less conservative synchronization criterion is established. Third, the desired estimator gain can be designed in terms of the solution to linear matrix inequalities (LMIs). The obtained conditions ensure the master–slave synchronization of CLSs under a longer sampling period than remarkable existing works. Finally, three numerical simulations of Chua’s circuit and neural network are provided to show the effectiveness and advantages of the proposed results.
85 citations
TL;DR: This article proposes an enhanced S-ELM by replacing the original principle component analysis (PCA) technique used in this algorithm with the correntropy-optimized temporal PCA (CTPCA), which is robust for outliers rejection and significantly improves the training speed.
Abstract: The stacked extreme learning machine (S-ELM) is an advanced framework of deep learning. It passes the ‘reduced’ outputs of the previous layer to the current layer, instead of directly propagating the previous outputs to the next layer in traditional deep learning. The S-ELM could address some large and complex data problems with a high accuracy and a relatively low requirement for memory. However, there is still room for improvement of the time complexity as well as robustness while using S-ELM. In this article, we propose an enhanced S-ELM by replacing the original principle component analysis (PCA) technique used in this algorithm with the correntropy-optimized temporal PCA (CTPCA), which is robust for outliers rejection and significantly improves the training speed. Then, the CTPCA-based S-ELM performs better than S-ELM in both accuracy and learning speed, when dealing with dataset disturbed by outliers. Furthermore, after integrating the extreme learning machine (ELM) sparse autoencoder (AE) method into the CTPCA-based S-ELM, the learning accuracy is further improved while spending a little more training time. Meanwhile, the sparser and more compact feature information are available by using the ELM sparse AE with more computational efforts. The simulation results on some benchmark datasets verify the effectiveness of our proposed methods.
TL;DR: The effectiveness and robustness of the proposed feedback controller toward uncertainties in the friction parameters and external disturbances are illustrated through simulation results.
Abstract: This paper proposes a robust feedback controller using Linear Matrix Inequalities (LMIs) formulation for the stabilization of an underactuated mechanical system, namely the Inertia Wheel Inverted Pendulum (IWIP), in its upright position. Such mechatronic system is subject to state constraints, external disturbances and norm-bounded parametric uncertainties. The main idea to solve the stabilization problem lies in the use of the S-procedure Lemma. Such problem is then transformed into a solving problem of Bilinear Matrix Inequalities (BMIs). Through the Schur complement Lemma and the Matrix Inversion Lemma, a linearization procedure is employed to transform the BMIs into LMIs. Some improvements and comparisons with other LMI-based design techniques without state constraints are developed and discussed. An extensive portfolio of numerical studies is presented. The effectiveness and robustness of the proposed feedback controller toward uncertainties in the friction parameters and external disturbances are illustrated through simulation results.
TL;DR: The boundary control is developed by introducing a smooth hyperbolic tangent function, an auxiliary system and a Nussbaum function and the uniformly bounded stability of the closed-loop system is analyzed and proven.
Abstract: In this study, we address the global stabilization issue of a flexible riser system subject to input saturation and external disturbances. Based on Lyapunov theory and backstepping method, the boundary control is developed by introducing a smooth hyperbolic tangent function, an auxiliary system and a Nussbaum function. The uniformly bounded stability of the closed-loop system is analyzed and proven without simplifying or discretizing the infinite-dimensional dynamics. Simulation results illustrate that the control designed works well in handling the input saturation and suppressing the vibration.
TL;DR: A static output feedback controller is designed for the closed-loop system to be admissible, based on new admissibility conditions of singular fractional-order systems expressed in a set of strict Linear Matrix Inequalities.
Abstract: This paper deals with the admissibility problem of singular fractional-order continuous time systems. It is based on new admissibility conditions of singular fractional-order systems expressed in a set of strict Linear Matrix Inequalities ( LMI s). Then, a static output feedback controller is designed for the closed-loop system to be admissible. Numerical examples are given to illustrate the proposed methods.
TL;DR: A maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed and a stochastic gradient algorithm is derived in order to compare it with the proposed ML- LM-R algorithm.
Abstract: This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.
TL;DR: By establishing a new differential inequality and constructing Lyapunov function, several useful criteria are derived analytically to realize finite-time synchronization for delay complex networks via aperiodically intermittent control.
Abstract: In this paper, we concern the finite-time synchronization problem for delayed dynamical networks via aperiodically intermittent control. Compared with some correspondingly previous results, the intermittent control can be aperiodic which is more general. Moreover, by establishing a new differential inequality and constructing Lyapunov function, several useful criteria are derived analytically to realize finite-time synchronization for delay complex networks. Additionally, as a special case, some sufficient conditions ensuring the finite-time synchronization for a class of coupled neural network are obtained. It is worth noting that the convergence time is carefully discussed and does not depend on control widths or rest widths for the proposed aperiodically intermittent control. Finally, a numerical example is given to demonstrate the validness of the proposed scheme.
TL;DR: A finite-time attitude controller combined with a new adaptive update law is designed and different from existing controllers, the proposed controller is inherently continuous and the chattering is effectively reduced.
Abstract: The continuous finite-time nonsingular terminal sliding mode (NTSM) attitude tracking control for rigid spacecraft is investigated. Firstly, a finite-time attitude controller combined with a new adaptive update law is designed. Different from existing controllers, the proposed controller is inherently continuous and the chattering is effectively reduced. Then, an adaptive model-free finite-time state observer (AMFFTSO) and an angular velocity calculation algorithm (AVCA) are developed to estimate the unknown angular velocity. The unique feature of the proposed method is that the finite-time estimation of angular velocity is achieved and no prior knowledge of quaternion derivative upper bound is needed. Next, based on the estimated angular velocity, a finite-time attitude controller with only attitude measurement is developed. Finally, some simulations are presented and the effectiveness of the proposed control scheme is illustrated.
TL;DR: A heuristic algorithm based on the Fireworks Algorithm is developed to obtain an optimized communication ratio, which greatly reduces the communication burden.
Abstract: This paper investigates the output formation-containment problem of the coupled heterogeneous linear systems under intermittent communication. The systems considered in this paper are more general in the sense that each system, whether a leader or a follower, has different dimension and different dynamic. Besides, each system only communicates with its neighbors intermittently. Based on the intermittent information, both the state-feedback and the output-feedback distributed control protocols are designed and a criterion is derived to calculate the lower bound of the communication ratio. Furthermore, a heuristic algorithm based on the Fireworks Algorithm is developed to obtain an optimized communication ratio, which greatly reduces the communication burden. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results.
TL;DR: A novel delayed fractional-order model of small-world networks is introduced and several topics related to the dynamics and control of such a network are investigated, such as the stability, Hopf bifurcations, and bIfurcation control.
Abstract: Bifurcation and control of fractional-order systems are still an outstanding problem. In this paper, a novel delayed fractional-order model of small-world networks is introduced and several topics related to the dynamics and control of such a network are investigated, such as the stability, Hopf bifurcations, and bifurcation control. The nonlinear interactive strength is chosen as the bifurcation parameter to analyze the impact of the interactive strength parameter on the dynamics of the fractional-order small-world network model. Firstly, the stability domain of the equilibrium is completely characterized with respect to network parameters, delays and orders, and some explicit conditions for the existence of Hopf bifurcations are established for the delayed fractional-order model. Then, a fractional-order Proportional-Derivative (PD) feedback controller is first put forward to successfully control the Hopf bifurcation which inherently happens due to the change of the interactive parameter. It is demonstrated that the onset of Hopf bifurcations can be delayed or advanced via the proposed fractional-order PD controller by setting proper control parameters. Meanwhile, the conditions of the stability and Hopf bifurcations are obtained for the controlled fractional-order small-world network model. Finally, illustrative examples are provided to justify the validity of the control strategy in controlling the Hopf bifurcation generated from the delayed fractional-order small-world network model.
TL;DR: The main objective of this paper is to propose an adaptive design of intrusion detection systems on the basis of Extreme Learning Machines that offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in a cost-effective manner.
Abstract: Despite the large volume of research conducted in the field of intrusion detection, finding a perfect solution of intrusion detection systems for critical applications is still a major challenge. This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems. The main objective of this paper is to propose an adaptive design of intrusion detection systems on the basis of Extreme Learning Machines. The proposed system offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in a cost-effective manner.
TL;DR: It has been observed that N LA – FOFPID controller outperforms NLA – FPID controller by offering much better performance and particularly in uncertain environment it offered very robust behavior.
Abstract: The main objective of this paper is to demonstrate that fractional order operators (differentiator/integrator) can increased the robustness of an intelligent controller. In this paper, a nonlinear adaptive fractional order fuzzy proportional integral derivative (NLA – FOFPID) controller is presented to control a nonlinear, coupled, multi-input and multi-output and uncertain system i.e. a 2-link planar rigid robotic manipulator with payload. The NLA – FOFPID controller is realized using non-integer order differentiator and integrator operators in nonlinear adaptive fuzzy proportional integral derivative (NLA – FPID) controller. The control objective is to track a reference trajectory in virtual industrial environment and under model uncertainties by minimizing the combination of integral of time weighted absolute error and integral of time weighted absolute change in controller output. The industrial environment was simulated by incorporating measurement noise, sinusoidal disturbance and model uncertainties. The gains of NLA – FPID and NLA – FOFPID controllers are optimized using recently reported Backtracking Search Algorithm. Intensive simulations were performed to assess the performances of NLA – FPID and NLA – FOFPID controllers for servo and regulatory mode. It has been observed that NLA – FOFPID controller outperforms NLA – FPID controller by offering much better performance. Particularly in uncertain environment it offered very robust behavior as compared to NLA – FPID controller. The sufficient stability conditions are also established for bounded-input and bounded-output stability of overall closed loop system by using small gain theorem.
TL;DR: An adaptive boundary iterative learning control (ILC) scheme for a two-link rigid–flexible manipulator with parametric uncertainties designed to deal with the unmodeled dynamics and other unknown external disturbances is proposed.
Abstract: To perform repetitive tasks, this paper proposes an adaptive boundary iterative learning control (ILC) scheme for a two-link rigid–flexible manipulator with parametric uncertainties. Using Hamilton׳s principle, the coupled ordinary differential equation and partial differential equation (ODE–PDE) dynamic model of the system is established. In order to drive the joints to follow desired trajectory and eliminate deformation of flexible beam simultaneously, boundary control strategy is added based on the conventional joints torque control. The adaptive iterative learning algorithm for boundary control scheme includes a proportional-derivative (PD) feedback structure and an iterative term. This novel controller is designed to deal with the unmodeled dynamics and other unknown external disturbances. Numerical simulations are provided to verify the performance of proposed controller in MATLAB.
TL;DR: An adaptive robust controller that simultaneously rotates the tower and moves the trolley is proposed for a tower crane and showed that the proposed controller consistently stabilized all system responses.
Abstract: An adaptive robust controller that simultaneously rotates the tower and moves the trolley is proposed for a tower crane. The robust behavior of the controller is derived through the sliding mode technique, and its adaptive performance is obtained based on the adaptive model-reference approach. The controller operated well regardless of the significant variation of system parameters, internal noises, and external disturbances. Specifically, the controller did not require a priori knowledge of cargo mass and friction factors because an adaptation mechanism is integrated to estimate system parameters. Emulating experimental results showed that the proposed controller consistently stabilized all system responses.
TL;DR: A new unified framework of finite-time and fixed-time bipartite consensus is built via some discontinuous control protocols based on the theory of differential inclusion and set-valued Lie derivative to fill the gap in studying FFTBC issues with discontinuous protocols.
Abstract: This paper investigates the issue of finite/fixed-time bipartite consensus (FFTBC) of multi-agent systems with signed graphs. A new unified framework of finite-time and fixed-time bipartite consensus is built via some discontinuous control protocols based on the theory of differential inclusion and set-valued Lie derivative. Under the structurally balanced or unbalanced signed graphs, the goal of FFTBC is reached by a common discontinuous controller with different control gains, which fills the gap in studying FFTBC issues with discontinuous protocols. Some numerical examples with comparisons are given to demonstrate the effectiveness of our designs.
TL;DR: The simulation results showed that the power allocation parameter has an important influence on the OP performance and a power allocation minimization problem is formulated.
Abstract: The outage probability (OP) performance of mobile device-to-device (D2D) cooperative networks with incremental amplify-and-forward (IAF) relaying and transmit antenna selection (TAS) is investigated in this paper. The exact closed-form OP expressions are derived for two TAS schemes. Based on the derived closed-form OP expressions, a power allocation minimization problem is formulated. Then through Monte Carlo simulations under different conditions, we have verified the accuracy of the derived OP expressions. The simulation results showed that the power allocation parameter has an important influence on the OP performance.
TL;DR: The FAIBSMC is proposed based on the concept of active control technique and the asymptotic stability of the controller is shown based on Lyapunov theorem and the finite time reaching to the sliding surfaces is proved.
Abstract: This paper precedes chaos control of fractional-order chaotic systems in presence of uncertainty and external disturbances. Based on some basic properties on fractional calculus and the stability theorems, we present a hybrid adaptive intelligent backstepping-sliding mode controller (FAIBSMC) for the finite-time control of such systems. The FAIBSMC is proposed based on the concept of active control technique. The asymptotic stability of the controller is shown based on Lyapunov theorem and the finite time reaching to the sliding surfaces is also proved. Illustrative and comparative examples and simulation results are given to confirm the effectiveness of the proposed procedure, which consent well with the analytical results.
TL;DR: Compared with the original Zhang neural network (ZNN) model, the proposed FTZNN model makes a breakthrough in the convergence performance (i.e., from infinite time to finite time).
Abstract: In this paper, a new design formula is presented to accelerate the convergence speed of a recurrent neural network, and applied to time-varying matrix square root finding in real time. Then, according to such a new design formula, a finite-time Zhang neural network (FTZNN) is proposed and investigated for finding time-varying matrix square root. In comparison with the original Zhang neural network (ZNN) model, the FTZNN model makes a breakthrough in the convergence performance (i.e., from infinite time to finite time). In addition, theoretical analyses of the design formula and the FTZNN model are provided in details. Comparative results further verify the superiority of the proposed FTZNN model to the original ZNN model for finding time-varying matrix square root.
TL;DR: This paper investigates the distributed tracking problem of nonlinear fractional-order multi-agent systems subject to heterogeneous control gains and a time-varying leader whose input is unknown and bounded over a general directed graph using the fractional Lyapunov direct method.
Abstract: By applying the fractional Lyapunov direct method, this paper investigates the distributed tracking problem of nonlinear fractional-order multi-agent systems subject to heterogeneous control gains and a time-varying leader whose input is unknown and bounded over a general directed graph. Due to the existence of heterogeneous control gains as well as a time-varying unknown leader in the nonlinear systems, the fractional-order dynamics of each agent is in essence heterogeneous. At first, a discontinuous distributed controller is constructed to guarantee that the distributed tracking control problem can be solved if some conditions are satisfied. Next, a continuous distributed controller is further proposed to eliminate the undesirable chattering phenomenon of the discontinuous controller, where the upper bound of the tracking error is uniformly bounded and can be made small enough by choosing the parameters appropriately. Finally, some simulation examples are presented to verify the effectiveness of the main results.
TL;DR: Three distributed adaptive coordinated control algorithms are proposed to ensure that the tracking errors for each follower can be bounded, and the first algorithm guarantees the higher leader-following control accuracy.
Abstract: This paper considers the distributed coordinated tracking control problems for multiple Euler–Lagrange systems with nonlinear uncertainties, external disturbances, and communication delays under the directed graph. First, distributed observers are designed such that all the followers can obtain the state information of the dynamic leader. Then, based on neural network and backstepping techniques, three distributed adaptive coordinated control algorithms are proposed to ensure that the tracking errors for each follower can be bounded. Compared with the first algorithm, the second algorithm guarantees the higher leader-following control accuracy. The third algorithm solves the chattering problem caused by discontinuous functions in the second algorithm. The closed-loop systems are investigated using the graph theory, Lyapunov theory, and Barbalat lemma. Finally, numerical examples and comparisons are provided to show the effectiveness and the performance of the proposed methods.
TL;DR: A data filtering based recursive least squares algorithm is proposed based on the data filtering technique and results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized most squares algorithm.
Abstract: Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input–output data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the data filtering technique, a data filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.