Dynamics and input–output feedback linearization control of a wheeled mobile cable-driven parallel robot

In comparison to the conventional parallel robots, cable-driven parallel robots (CDPRs) have generally superior features such as simple production technology, low energy consumption, large workspace, high payload to moving weight ratio, and also low cost. On the other hand, a wheeled mobile robot (WMR) which is capable of covering a vast area can be used when no specific space is designated for the stationary accessories of a robot. In this paper, the integration of a CDPR with a WMR is proposed to overcome some of the issues related to each of these robots. The kinematic equations of the robot are presented. To derive the dynamic equations, Gibbs–Appel (G–A) formulation is used, which in contrary to the Lagrange formulation benefits from advantages of quasi-velocities over generalized coordinates as well as not requiring Lagrange multipliers. The dynamic equations of the two parts are coupled, and the interacting effects are observable from the governing equations. By considering non-holonomic wheels for the robot, internal dynamics appears in the equations. However, based on some conditions, the equations are input–output linearizable via a static feedback. The platform trajectory is designed based on the given end-effector trajectory. The effectiveness of the controller is shown through simulations and experimental tests.     

Synchronization of nonlinear heterogeneous cooperative systems using input–output feedback linearization

In this paper, input–output feedback linearization is used to design distributed controls for multi-agent systems with nonlinear and heterogeneous non-identical dynamics. Using feedback linearization, the nonlinear and heterogeneous dynamics of agents are transformed to identical linear dynamics and non-identical internal dynamics. Based on the dependence of agent outputs on agent inputs, feedback linearization may lead to a first-order or high-order tracking synchronization problem. The controller for each agent is designed to be fully distributed such that each agent only requires its own information and the information of its neighbors. The effectiveness of the proposed control protocols are verified by simulation on a microgrid test system.     

Predictor–observer-based control of systems with multiple input/output delays

This paper deals with the control of single-input–single-output linear time-delayed systems, when there are different delays in the output/input transfer function. The process can be disturbed and unstable. A new control structure is proposed, solving the problem in several steps. First, by using a stable predictor/observer the plant is stabilized, regardless the delays. On the stable plant, the multidelay plant model is considered as a single delay plant with additional disturbances. The problem of disturbance rejection is solved by using a disturbance observer approach. Finally, output tracking is achieved by a two degrees of freedom controller.     

Tracking control of non-linear systems using sliding surfaces,with application to robot manipulators†

A methodology of feedback control is developed to achieve accurate tracking in a. class of non-linear, time-varying systems in the presence of disturbances and para meter variations. The methodology uses in its idealized form piecewise continuous feedback control, resulting in the state trajectory sliding along a time-varying sliding surface in the state space. This idealized control law achieves perfect tracking; however, non-idealities in its implementation result in the generation of an undesirable high-frequency component in the state trajectory. To rectify this, it is shown how continuous control laws may be used to approximate the discontinuous control law to obtain robust tracking to within a prescribed accuracy without generating undesirable high-frequency signal. The method is applied to the control of a two-link manipulator handling variable loads in a flexible manufacturing system environment.     

A linearized input–output representation of flexible multibody systems for control synthesis

In this paper, a linearized input–output representation of flexible multibody systems is proposed in which an arbitrary combination of positions, velocities, accelerations, and forces can be taken as input variables and as output variables. The formulation is based on a nonlinear finite element approach in which a multibody system is modeled as an assembly of rigid body elements interconnected by joint elements such as flexible hinges and beams. The proposed formulation is general in nature and can be applied for prototype modeling and control system analysis of mechatronic systems. Application of the theory is illustrated through a detailed model development of an active vibration isolation system for a metrology frame of a lithography machine.     

Modeling of input–output relationships for a plasma spray coating process using soft computing tools

To automate any manufacturing process, its input–output relationships are to be known in both forward and reverse directions. The present work aims to correlate input process parameters with various responses of a plasma spray coating process. Statistical regression analysis had been carried out previously for this process based on the data collected through central composite design of experiments to establish input–output relationships in forward direction. However, the said relationships could not be accurately determined in reverse direction using the obtained regression equations due to the presence of a non-square transformation matrix. Soft computing-based approaches had been developed to model the process in both forward as well as reverse directions. The performances of the developed approaches had been tested on different cases obtained through real experiments. A comparative study had been made of these developed approaches in terms of accuracy in predictions.     

Optimization of multiple input–output fuzzy membership functions using clonal selection algorithm

A clonal selection algorithm (CLONALG) inspires from clonal selection principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed CLONALG program for a multiple input–output (MI–O) fuzzy system. In this study, GA and binary particle swarm optimization (BPSO) are used for implementing the proposed method as well and they are compared. It has been shown that using clonal selection algorithm is advantageous for finding optimum values of fuzzy membership functions     

Robust input–output linearisation using uncertainty and disturbance estimation

This article proposes a new approach to robustify an input–output linearisation controller. The robustification is achieved by estimating the uncertainties and external unmeasurable disturbances using a novel uncertainty and disturbance estimator. A significant feature of the proposed approach is that it does not need any information about the uncertainties. The stability of the system and the estimator is established. Effectiveness of the proposed approach is demonstrated through application to the wing rock motion control problem.     

Non-linear control by input–output state variable feedback pole assignment

This article considers pole assignment control of non-linear dynamic systems described by state-dependent parameter (SDP) models. The approach follows from earlier research into linear proportional-integral-plus methods but, in SDP system control, the control coefficients are updated at each sampling instant on the basis of the latest SDP relationships. Alternatively, algebraic solutions can be derived off-line to yield a practically useful control algorithm that is relatively straightforward to implement on a digital computer, requiring only the storage of delayed system variables, coupled with straightforward arithmetic expressions in the control software. Although the analysis is limited to the case when the open-loop system has no zeros, time delays are handled automatically. This article shows that the closed-loop system reduces to a linear transfer function with the specified (design) poles. Hence, assuming pole assignability at each sample, global stability of the non-linear system is guaranteed at the design stage.     

Posture control of robot manipulators with fuzzy voice commands using a fuzzy coach–player system

This paper presents a method of controlling robot manipulators with fuzzy voice commands. Recently, there has been some research on controlling robots using information-rich fuzzy voice commands such as 'go little slowly' and learning from such commands. However, the scope of all those works was limited to basic fuzzy voice motion commands. In this paper, we introduce a method of controlling the posture of a manipulator using complex fuzzy voice commands. A complex fuzzy voice command is composed of a set of fuzzy voice joint commands. Complex fuzzy voice commands can be used for complicated maneuvering of a manipulator, while fuzzy voice joint commands affect only a single joint. Once joint commands are learned, any complex command can be learned as a combination of some or all of them, so that, using the learned complex commands, a human user can control the manipulator in a complicated manner with natural language commands. Learning of complex commands is discussed in the framework of fuzzy coach–player model. The proposed idea is demonstrated with a PA-10 redundant manipulator.     

Shared control architecture based on RFID to control a robot arm using a spontaneous brain–machine interface

This paper describes a shared control architecture combining a Brain–Machine Interface (BMI) with Radio-frequency Identification (RFID) technology to control a robot arm in pick and place operations. A non-invasive spontaneous BMI capable of distinguishing between three different mental tasks has been designed. Using the BMI, the user can control the robot in order to perform complex actions (e.g. pick and place operations). RFID tags have been placed in the experimental setup to give information about the position of the objects in the scene. With this information, the user is able to pick and place the objects with a robot arm by performing simple commands: move left, move right, pick or place, with the only help of the BMI. Four volunteers have successfully controlled the robot arm, and time and accuracy have been measured.     

Control of a 2 DoF robot using a Brain–Machine Interface

In this paper, a non-invasive spontaneous Brain–Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University of Elche (Spain). Two control strategies are compared: hierarchical and directional control. The experimental test (performed by four users) consists of reaching four targets. The errors and time used during the performance of the tests are compared in both control strategies (hierarchical and directional control). The advantages and disadvantages of each method are shown after the analysis of the results. The hierarchical control allows an accurate approaching to the goals but it is slower than using the directional control which, on the contrary, is less precise. The results show both strategies are useful to control this planar robot. In the future, by adding an extra device like a gripper, this BMI could be used in assistive applications such as grasping daily objects in a realistic environment. In order to compare the behavior of the system taking into account the opinion of the users, a NASA Tasks Load Index (TLX) questionnaire is filled out after two sessions are completed.     

Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot

Detecting, locating and repairing faults is a hard task. This holds especially in cases where dependent failures occur in practice. In this paper we present a methodology which is capable of handling dependent failures. For this purpose we extend the model-based diagnosis approach by explicitely representing knowledge about such dependencies which are stored in a failure dependency graph. Beside the theoretical foundations we present algorithms for computing diagnoses and repair actions that are based on these extensions. Moreover, we introduce a case study which makes use of a larger control program of an autonomous and mobile robot. The case study shows that the proposed approach can be effectively used in practice.     

Simultaneous dissolved oxygen and glucose regulation in fed-batch methionine production using decoupled input–output linearizing control

Methionine is one of the essential amino acids produced by fed-batch fermentation. The synthesis of methionine at the cellular level is strictly regulated and its process dynamics shows a nonlinear interaction between dissolved oxygen and glucose concentration. For controlling this process, a decoupled input–output linearizing controller (DIOLC) is derived. The model used for this purpose contains an exponential kinetic structure for describing the nonlinearities and metabolic switching function for describing oxygen dependency. The control system is square having two inputs and two outputs. The zero dynamics stability for internal variables and error convergence is proved. The performance of DIOLC is examined under high and low oxygen demand conditions. Four case studies are used to demonstrate that the DIOLC action is decoupled. The DIOLC also exhibited robust performance even for random variations up to ±20% in some parameters. In simulated experiments, using DIOLC produces 16 g l^?1 of methionine. The performance of PI controllers under identical conditions is given for comparison.     

Stability and performance analysis of linear positive systems with delays using input–output methods

It is known that input–output approaches based on scaled small-gain theorems with constant D-scalings and integral linear constraints are non-conservative for the analysis of some classes of linear positive systems interconnected with uncertain linear operators. This dramatically contrasts with the case of general linear systems with delays where input–output approaches provide, in general, sufficient conditions only. Using these results, we provide simple alternative proofs for many of the existing results on the stability of linear positive systems with discrete/distributed/neutral time-invariant/-varying delays and linear difference equations. In particular, we give a simple proof for the characterisation of diagonal Riccati stability for systems with discrete-delays and generalise this equation to other types of delay systems. The fact that all those results can be reproved in a very simple way demonstrates the importance and the efficiency of the input–output framework for the analysis of linear positive systems. The approach is also used to derive performance results evaluated in terms of the L ₁-, L ₂- and L _∞-gains. It is also flexible enough to be used for design purposes.     

A simple algorithm for training fuzzy systems using input–output data

This paper proposes a simple algorithm for training fuzzy systems from numerical data. The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method.     

State space realization of bilinear continuous-time input–output equations

This paper studies the realizability property of continuous-time bilinear input–output (i/o) equations in the classical state space form. Constraints on the parameters of the bilinear i/o model are suggested that lead to realizable models. The paper proves that the 2nd order bilinear i/o differential equation, unlike the discrete-time case, is always realizable in the classical state space form. The complete list of 3rd and 4th order realizable i/o bilinear models is given and two subclasses of realizable i/o bilinear systems are suggested. Our conditions rely basically upon the property that certain combinations of coefficients of the i/o equations are zero or not zero. We provide explicit state equations for all realizable 2nd and 3rd order bilinear i/o equations, and for one realizable subclass of bilinear i/o equations of arbitrary order.