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This paper aims to investigate “corporate governance” of the English East India Company (EIC) in the late 17th century through a case study of the Tonkin factory (1672–1697).

The paper draws upon British primary materials relating to the Tonkin factory to examine and analyze the EIC’s style of management in Tonkin (Vietnam) and Bantam (Java). Qualitative and comparative methods are applied to the analysis of reports, records and letters written by EIC staff.

The paper finds that the EIC faced principal-agent problems as it had difficulties administering its distant agents and subsidiaries in the 17th century. London was strategically weakened, both by the limiting power of regional headquarters and by its use of experienced factors. Before 1682, London failed to temper the Bantam Council’s influence, and there were serious internal conflicts and power struggles between English Tonkin employees seeking to improve their positions. After 1686, London successfully forced Madras to adopt a noninterventionist stance in Tonkin’s business, but it faced the problem of “adverse selection.”

This paper provides evidence from the Tonkin factory (1672–1697) to show the EIC’s governance in the perspective of the agency theory.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

The effective and efficient motivation of the sales personnel affects the sales of a firm directly. The aim of this paper is to study the incentive effects of different compensation contracts under the framework of multi‐agent principal agent model, and it finds that the optimal contract is not the one that ties one salesperson's compensation to his own performance, but the one that ties his compensation to all the salespersons' performance. Factors that influence the incentive degree are also discussed. The purpose of this article is to design a reasonable incentive contract for salespersons where there are competitions between them.

A multi‐agent model where the efforts of one agent harm the performance of the other agent is established.

The optimal compensation of a salesperson is always composed of two parts: an incentive for an agent to improve his own performance and a disincentive for the agent to harm his colleague's performance, provided that there is a competition relationship between the two agents.

This model applies only to the rewards incentive of multi‐agents with competitive relationships.

The conclusion could be used anywhere when there are two agents with one's behavior harming the other's performance.

A multi‐agent model where the efforts of one agent harm the performance of the other agent is established to study the compensation design problems for agents.

Permanent magnet synchronous spherical motors can have wide use in robotics and industrial automation. They enable three-DOF omnidirectional motion of their rotor. They are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. Unlike conventional synchronous motors, permanent magnet synchronous spherical motors consist of a fixed inner shell, which is the stator, and a rotating outer shell, which is the rotor. Their dynamic model is multivariable and strongly nonlinear. The treatment of the associated control problem is important.

In this paper, the multivariable dynamic model of permanent magnet synchronous spherical motors is analysed, and a nonlinear optimal (H-infinity) control method is developed for it. Differential flatness properties are proven for the spherical motors’ state-space model. Next, the motors’ state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a time-varying operating point, which is defined by the present value of the motors’ state vector and by the last sampled value of the control input vector. For the approximately linearized model of the permanent magnet synchronous spherical motors, a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains, an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. Finally, the performance of the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops.

Due to the nonlinear and multivariable structure of the state-space model of spherical motors, the solution of the associated nonlinear control problem is a nontrivial task. In this paper, a novel nonlinear optimal (H-infinity) control approach is proposed for the dynamic model of permanent magnet synchronous spherical motors. The method is based on approximate linearization of the motor’s state-space model with the use of first-order Taylor series expansion and the computation of the associated Jacobian matrices. Furthermore, the paper has introduced a different solution to the nonlinear control problem of the permanent magnet synchronous spherical motor, which is based on flatness-based control implemented in successive loops.

The presented control approaches do not exhibit any limitations, but on the contrary, they have specific advantages. In comparison to global linearization-based control schemes (such as Lie-algebra-based control), they do not make use of complicated changes of state variables (diffeomorphisms) and transformations of the system's state-space description. The computed control inputs are applied directly to the initial nonlinear state-space model of the permanent magnet spherical motor without the intervention of inverse transformations and thus without coming against the risk of singularities.

The motion control problem of spherical motors is nontrivial because of the complicated nonlinear and multivariable dynamics of these electric machines. So far, there have been several attempts to apply nonlinear feedback control to permanent magnet-synchronous spherical motors. However, due to the model’s complexity, few results exist about the associated nonlinear optimal control problem. The proposed nonlinear control methods for permanent magnet synchronous spherical motors make more efficient, precise and reliable the use of such motors in robotics, electric traction and several automation systems.

The treated research topic is central for robotic and industrial automation. Permanent magnet synchronous spherical motors are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. The solution of the control problem for the nonlinear dynamic model of permanent magnet synchronous spherical motors has many industrial applications and therefore contributes to economic growth and development.

The proposed nonlinear optimal control method is novel compared to past attempts to solve the optimal control problem for nonlinear dynamical systems. Unlike past approaches, in the new nonlinear optimal control method, linearization is performed around a temporary operating point, which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector and not at points that belong to the desirable trajectory (setpoints). Besides, the Riccati equation which is used for computing the feedback gains of the controller is new, and so is the global stability proof for this control method. Compared to nonlinear model predictive control, which is a popular approach for treating the optimal control problem in industry, the new nonlinear optimal (H-infinity) control scheme is of proven global stability, and the convergence of its iterative search for the optimum does not depend on initial conditions and trials with multiple sets of controller parameters. It is also noteworthy that the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems which can be transformed into the linear parameter varying form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes, which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions. Furthermore, the second control method proposed in this paper, which is flatness-based control in successive loops, is also novel and demonstrates substantial contribution to nonlinear control for robotics and industrial automation.

The purpose of this paper is to present a time‐domain technique to compute the electromagnetic wave field and to reconstruct the permittivity and electric conductivity profile of a one‐dimensional slab of finite length.

The forward scattering problem is solved by a Green's function formulation to generate synthetic data that are used as a testbed for the inversion scheme. The inverse scattering problem is solved by reconstructing the unknown permittivity and electric conductivity profile of the medium with the help of an invariant embedding method.

The Green's operator maps the incident field on either side of the medium to the field at an arbitrary observation point inside the slab and hence, the internal fields can be computed directly without computing the wave field throughout the entire medium. The invariant embedding method requires a finite time trace of reflection data and therefore it is suitable for reconstructing the material parameters in real‐time.

The implemented methods have been validated against synthetic and measured time domain reflectometry data.

This paper fulfils an identified need to determine unknown one‐dimensional profiles and thus plays an important role in electromagnetics, non‐destructive testing, and geophysics.

To control one of the joints during the actual movement of the hydraulically driven quadruped robot, all the other joints in the leg need to be locked. Once the joints are unlocked, there is a coupling effect among the joints. Therefore, during the normal exercise of the robot, the movement of each joint is affected by the coupling of other joints. This brings great difficulties to the coordinated motion control of the multi-joints of the robot. Therefore, it is necessary to reduce the influence of the coupling of the hydraulically driven quadruped robot.

To solve the coupling problem with the joints of the hydraulic quadruped robot, based on the principle of mechanism dynamics and hydraulic control, the dynamic mathematical model of the single leg mechanism of the hydraulic quadruped robot is established. On this basis, the coupling dynamics model of the two joints of the thigh and the calf is derived. On the basis of the multivariable decoupling theory, a neural network (NN) model reference decoupling controller is designed.

The simulation and prototype experiment are carried out between the thigh joint and the calf joint of the hydraulic quadruped robot, and the results show that the proposed NN model reference decoupling control method is effective, and this method can reduce the cross-coupling between the thigh and the calf and improve the dynamic characteristics of the single joint of the leg.

The proposed method provides technical support for the mechanical–hydraulic cross-coupling among the joints of the hydraulic quadruped robot, achieving coordinated movement of multiple joints of the robot and promoting the performance and automation level of the hydraulic quadruped robot.

On the basis of the theory of multivariable decoupling, a new decoupling control method is proposed, in which the mechanical–hydraulic coupling is taken as the coupling behavior of the hydraulic foot robot. The method reduces the influence of coupling of system, improves the control precision, realizes the coordinated movement among multiple joints and promotes the popularization and use of the hydraulically driven quadruped robot.

The passive source localization (PSL) problem using angles of arrival (AOA), time differences of arrival (TDOA) or gain ratios of arrival (GROA) is generally nonlinear and nontrival. In this research, the purpose of this paper is to design an accurate hybrid source localization approach to solve the PSL problem. The inspiration is drawn from the fact that the bearings, TDOAs and GROAs are complementary in terms of their geometry properties.

The maximum-likelihood (ML) method is reexamined by using hybrid measurements. Being assisted by the bearings, a new hybrid weighted least-squares (WLS) method is then proposed by jointly utilizing the bearing, TDOA and GROA measurements.

Theoretical performance analysis illustrates that the mean-square error of the ML or WLS method can attain the Cramér-Rao lower bound for Gaussian noise over small error region. However, the WLS method has much lower computational complexity than the ML algorithm. Compared with the AOA-only, TDOA-only, AOA-TDOA, TDOA-GROA methods, the localization accuracy can be greatly improved by combining the AOAs, TDOAs and GROAs, especially for some specific geometries.

A novel bearing-assisted TDOA-GROA method is proposed for source localization, and a new hybrid WLS estimator is presented inspired from the fact that the bearings, TDOAs and GROAs are complementary in terms of their geometry properties.

Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies.

The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020.

Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar.

This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling.

First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation.

The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased.

The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

The purpose of this paper is to analyze the nature of returns and volatility spillovers between exchange rates and stock price in the IBSA nations (India, Brazil, South Africa).

The study uses VAR framework and the recently proposed Spillover measure of Diebold and Yilmaz to examine the returns and volatility spillover between exchange rates and stock prices of IBSA nations. In addition, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.

The results of multivariate GARCH model suggests the integration between stock and foreign exchange markets and indicates the existence of bi‐directional volatility spillover between stock and foreign exchange markets in the IBSA countries. Spillover results using the Diebold Yilmaz model suggest the bi‐directional contribution between stock and foreign exchange market, in terms of both returns and volatility spillovers. Overall, results confirm the presence of returns and volatility spillovers within the IBSA nations and, in particular, the stock markets play a relatively more important role than foreign exchange markets in the first and second moment interactions and spillovers.

The market participants may consider the relationship between the exchange rate and stock index to predict the future movement of each other effectively. Multinational companies interested in exchange rate forecasting may consider the stock market as an important attribute. There is an interesting implication for portfolio managers too because of the spillover stock and foreign exchange markets. This knowledge would help to create a fund which performs well. Moreover, the paper can help regulators and policy makers in IBSA nations to understand the structure of the market in a better way and then design their policies.

The study contributes to the literature by extending the existing studies on the spillover between stock price and exchange rate by investigating the issue for three emerging economies, India, Brazil and South Africa. Unlike most studies in the literature which focus on multivariate GARCH model, this is the first study which explores the issue of returns and volatility spillover between the stock prices and the exchange rates using spillover measure of Diebold and Yilmaz and much longer and recent daily data. Moreover, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.