Sliding mode control design using generalized relative degree approach: Aerospace application

Relative degree (RD) approach is a powerful tool for obtaining system's input-output dynamics used for output tracking controller designs of minimum phase systems. Designs using the RD alone can fail due both to insufficient control authority in minimum phase systems, and instability of internal/zero dynamics attributed to nonminimum phase systems. A novel definition and a concept of Practical Generalized RD (PGRD) are proposed in this paper and are used in concert with Sliding Mode Control (SMC) to compensate for system perturbations in minimum phase systems. The use of known Generalized Relative Degree (GRD) in nonminimum phase systems allows for the elimination of internal dynamics. However, instability that emerges in the corresponding control dynamic extension is defeating any output tracking controller design. A novel methodology of using GRD for designing continuous SMC in nonminimum phase systems is presented. An algorithm for generating a bounded solution of the unstable dynamic extension is proposed and used in concert with SMC, allowing robust control design for nonminimum phase systems. The efficacy of the proposed GRD-based approaches is demonstrated on a minimum and nonminimum phase rocket attitude control problem both analytically and via simulation.     

Fixed-time leader-following consensus of multiple uncertain nonholonomic systems: An adaptive distributed observer approach

This paper discusses the fixed-time leader-following consensus problem for multiple uncertain nonholonomic systems, which are widely used in engineering models. According to our literature review, either the system is assumed to be known, or the uncertainty only contains state information, which does not meet the actual requirements. For this reason, this paper investigates more general nonholonomic systems with uncertainties driven by inputs and states. First, a fixed-time adaptive distributed observer is proposed to estimate the leader’s state and structural parameters, which ensures that the estimation errors converge to zero within a fixed time. Second, two regulator equations based on the idea of cooperative output regulation are constructed, and a novel observer-based distributed switching control law is proposed. This control law overcomes the nonholonomic constraints and appropriately relaxes the assumptions of uncertain functions in the existing references. Finally, the simulation results verify the effectiveness of the proposed control scheme.     

Fuzzy adaptive event-triggered control for uncertain nonlinear system with prescribed performance: A combinational measurement approach

This paper addresses the adaptive fuzzy event-triggered control (ETC) problem for a class of nonlinear uncertain systems with unknown nonlinear functions. A novel ETC approach that exhibits a combinational triggering (CT) behavior is proposed to update the controller and fuzzy weight vectors, achieving the non-periodic control input signals for nonlinear systems. A CT-based fuzzy adaptive observer is firstly constructed to estimate the unmeasurable states. Based on this, an output feedback ETC is proposed following the backstepping and error transformation methods, which ensures the prescribed dynamic tracking (PDT) performance. The PDT performance indicates that the transient bounds, over-shooting and ultimate values of tracking errors are fully determined by the control parameters and functions chosen by users. The closed-loop stability is guaranteed under the framework of impulsive dynamic system. Besides, the Zeno phenomenon is circumvented. The theoretical analysis indicates that the proposed scheme guarantees control performance while considerably reducing the communication resource utilization and controller updating frequency. Finally, the numerical simulations are conducted to verify the theoretical findings.     

Adaptive disturbance attenuation for a class of uncertain nonlinear systems: A double-gain nonlinear observer method

This paper is concerned with the problem of adaptive disturbance attenuation for a class of nonlinear systems. The traditional adaptive methods are almost impossible to compensate the time-varying unknown disturbance by designing parameter adaptive laws without a priori knowledge about the bounds of external disturbances. To solve the problem, a new strategy is proposed by constructing an augmented system where the external disturbance is considered as another component of the augmented state vector. Based on this, a double-gain nonlinear observer is employed to estimate the state of the augmented nonlinear system. Further, an output feedback control strategy is designed, and it is proved that the proposed strategy ensures that all the signals are bounded and the tracking error exponentially converges to an adjustable compact set. Finally, an example is performed to demonstrate the validity of the proposed scheme.     

Output containment control of heterogeneous multi-agent systems with leaders of bounded inputs: An adaptive finite-time observer approach

In this paper, we address the problem of output containment control of general linear multi-agent systems (MASs). The MAS under consideration is comprised by multiple followers and multiple leaders, all with heterogeneous dynamics. In particular, the leaders’ dynamics are subject to heterogeneous non-zero (possibly persistent) but bounded inputs, which are not measurable for any follower agent, making the associated distributed control design problem rather challenging. A new distributed observer-based containment control protocol is proposed to overcome associated challenges. It consists of two hierarchical layers including (i) the first layer of adaptive finite-time cooperative observer responsible for estimating the convex-hull signals formed by multiple leaders’ states through inter-agent collaboration; and (ii) the second layer of distributed state-feedback controller responsible for local tracking control through a modified output regulation technique. Important novelties of the proposed protocol are that (i) it deals with MASs with not only heterogeneous followers but also heterogeneous leaders; (ii) exact output containment control performance can be achieved in the presence of unmeasurable leaders’ inputs and unknown connectivity of communication network; and (iii) associated solvability conditions are formulated as linear matrix inequalities plus linear algebraic equations, which can be tested and solved effectively via efficient semi-definite programming. The developed theoretical results are demonstrated both rigorously using Lyapunov methods and through numerical simulations.     

Sampled-data sliding mode observer for robust fault reconstruction: A time-delay approach

A sliding mode observer in the presence of sampled output information and its application to robust fault reconstruction is studied. The observer is designed by using the delayed continuous-time representation of the sampled-data system, for which sufficient conditions are given in the form of linear matrix inequalities (LMIs) to guarantee the ultimate boundedness of the error dynamics. Though an ideal sliding motion cannot be achieved in the observer when the outputs are sampled, ultimately bounded solutions can be obtained provided the sampling frequency is fast enough. The bound on the solution is proportional to the sampling interval and the magnitude of the switching gain. The proposed observer design is applied to the problem of fault reconstruction under sampled outputs and system uncertainties. It is shown that actuator or sensor faults can be reconstructed reliably from the output error dynamics. An example of observer design for an inverted pendulum system is used to demonstrate the merit of the proposed methodology compared to existing sliding mode observer design approaches.     

Robust stabilization of nonlinear time delay systems: A complete type functionals approach

The robust stabilization of some classes of nonlinear delay systems with nominal linear delay system is addressed. The form of the controller is not an a priori proposal, but it is the result of a synthesis relying on the use of complete type Lyapunov–Krasovskii functionals, leading to distributed delay linear or nonlinear robust control laws. Simulation results of the stabilization of a chemical refining process demonstrate the good performance of the proposed approaches.     

Model-Free distributed optimal consensus control of nonlinear multi-agent systems: A graphical game approach

In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm.     

Iterative dynamic linearization and identification of a nonlinear learning controller: A data-driven approach

In this article, a nonlinear iterative learning controller (NILC) is developed using an iterative dynamic linearization (IDL) and a parameter iterative learning identification technique. First, the ideal NILC is transformed into a linear parameterized form by using a controller-oriented compact form IDL (controller-CFIDL) technique. Then an iterative learning identification approach is presented for tuning the parameters of the proposed controller using real-time I/O data. For the sake of analysis, a linear data model of the nonlinear plant is obtained by using the system-oriented IDL technology and a corresponding system parameter identification algorithm is developed in iteration domain. The convergence analysis is provided for the dynamically linearized nonlinear and nonaffine discrete-time system. The results are further extended by using a controller-oriented partial form iterative dynamic linearization (controller-PFIDL) method to gain a higher-order NILC utilizing additional control information from previous iterations. Simulations of two examples show the effectiveness of the proposed methods.     

Controller design for 2-D stochastic nonlinear Roesser model: A probability-dependent gain-scheduling approach

This note is concerned with the static output feedback control problem for two-dimensional (2-D) uncertain stochastic nonlinear systems. The systems under consideration are subjected to time delays, multiplicative noises and randomly occurring missing measurements. A random variable sequence following the Bernoulli distribution with time-varying probability is employed to character the missing measurements which are assumed to occur in a random way. A new gain-scheduling method based on the time-varying probability parameter is proposed to accomplish the design task. By constructing a suitable Lyapunov functional, sufficient conditions to guarantee the systems to be mean-square asymptotically stable are established. The addressed 2-D controller design problem can be reduced to a convex optimization problem by some mathematical techniques. In the last section, a numerical example and the comparative analysis are provided to illustrate the efficiency of our proposed design approach.     

Prescribed performance bipartite consensus for nonlinear agents with antagonistic interactions: A PI transformation approach

The leaderless, prescribed performance consensus problem for groups of agents with antagonistic interactions is addressed for the first time in this paper. We consider agents modeled by pure feedback nonlinear systems with unknown dynamics and an agent communication network described by a signed digraph with a directed spanning tree. A new proportional and integral (PI) variable transformation is proposed that enables the solution of the problem of leaderless bipartite consensus with prescribed performance by recasting it into a regulation problem with prescribed performance, which in turn we solve by a low complexity distributed control law. The algorithm guarantees uniform boundedness of all closed-loop signals and prescribed performance for the bipartite consensus error. Simulations verify the validity of our theoretical analysis.     

Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: A DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi-agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov–Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme.     

Adaptive fuzzy tracking control for input and output constrained stochastic nonlinear systems: A NM-based approach

This paper investigates the tracking control problem for output constrained stochastic nonlinear systems under quantized input. The main challenge of considering such dynamics lies in the fact that theirs have both input and output constraints, making the standard backstepping technique fail. To address this challenge, the introduction of nonlinear mapping transforms the constrained nonlinear systems into unconstrained nonlinear systems, which not only avoids the emergence of feasibility conditions but also simplifies the structure of designed controller. The obstacle caused by quantized input is successfully resolved by exploiting the decomposition of hysteresis quantizer. Additionally, the uncertain nonlinearities are approximated by fuzzy logic systems during the control design process. Under the proposed quantized tracking control scheme, the output tracking error converges to an arbitrarily small neighborhood of origin and all signals in the closed-loop system remain bounded in probability. Simultaneously, it can make sure that the output constraint isn’t violated. Ultimately, both a numerical example and a practical example are provided to clarify the effectiveness of the control strategy.     

A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains

The integration of selling and fulfillment processes triggered by omni-channels is transforming the retailer’s operations management. In this context, there is a lack of research regarding the connection between digital and physical worlds in retail supply chains. This paper aims to propose a data-driven approach that combines machine-learning demand forecasting and operational planning simulation-based optimization to adaptively synchronize demand and supply in omni-channel retail supply chains. The findings are substantiated through the application of the approach in an omni-channel retail supply chain. The combination of clustering and neural networks improved demand forecast, supporting an assertive identification of demand volume and location. Simulation-based optimization allowed for the definition of which facility would serve identified demands most effectively. The approach reduced fulfillment lead time, mitigated backorders arising from incompatible product´s supply and demand, and lowered operational costs, which are key performance indicators in today’s competitive retail markets.     

Sliding mode controller design for MIMO nonlinear systems: A novel power rate reaching law approach for improved performance

This paper proposes a novel approach to the design of reaching law based on Sliding Mode Controller (SMC) for multi input multi output (MIMO) non-linear systems so as to overcome the drawbacks associated with conventional reaching law based SMC design strategies. The modification is proposed with an aim to completely eliminate chattering, to ensure control inputs within admissible limits and to guarantee fast response when SMC is used. Modification to conventional power rate reaching law is the point of interest here in order to ensure complete elimination of chattering. Two different modifications to power rate reaching law are presented which incorporate control constraints during controller design so that admissible control input limits are not exceeded. The first modified method ensures limited control effort as well as complete chattering free response, but does not improve the reaching characteristics. So a second adaptive modification to power rate reaching law is also presented here. This method ensures fast reaching to the sliding surface along with properties of complete elimination of chattering and bounded control inputs. However, as in power rate reaching law these modified methods retain the limitation of not possessing robustness properties. The method is applied to a three degree of freedom robotic arm which is typically a non-linear MIMO system. The ability of the presented method to satisfy attributes, viz., chattering free operation, bounded control inputs and fast response is compared with the performance of various reaching law methods available in the literature. The performance of the proposed method is validated through simulation studies on the robotic arm example.     

A comprehensive survey of IS undergraduate degree courses in the UK

There is little conclusive evidence about the state of the IS curriculum in the UK. This paper presents the results of a detailed survey about the provision of Information Systems (IS) undergraduate degree courses in the UK using the newly developed IS 2010 curriculum guidelines as its basis. IS 2010 identifies three main categories of knowledge that underpin the essence of IS as an academic subject: IS Specific Knowledge and Skills, Domain Fundamentals and Foundational Knowledge and Skills. Our desire to offer a holistic and conclusive representation of the curriculum is supported by the development of an IS curriculum survey framework that caters for the mapping of every subject that populates the IS curricula of undergraduate degrees in the UK, including subjects that are generic or are derived from other hierarchical disciplines.     

农业发展的新思路:第二农业

随着中国加入世界贸易组织,农业生产部门在面临着前所未有的挑战的同时,也存在一些难得的发展机遇。如果能在农业发展思路上更新观念,做到扬长避短,则我国的农业生产水平有可能迈上一个新台阶。笔者认为发展第二农业是提高我国农业生产水平、保障农业可持续发展的     

Innovation in UK higher education: A panel data analysis of undergraduate degree programmes

In the UK, higher education is increasingly a marketised service sharing many characteristics with other professional services such as legal, medical or financial services. With marketisation comes competition, and the need for Higher Education Institutions (HEIs) to develop and maintain strong programmes to attract and retain high-class faculty and fee-paying students. Here, we consider the drivers of programme innovation ? i.e. the introduction of new programmes and the withdrawal of existing programmes ? in UK universities. Our focus is on undergraduate programmes as these account for three-quarters of all student enrolments. Using panel data for UK universities we identify significant resource, internationalisation and business engagement effects. Financial stringency and more extensive international market engagement both encourage programme introduction. Collaboration with businesses has offsetting effects depending on the nature of the interaction. The results have both strategic and systemic implications.