Search

This article discusses control and coordination in biochemical networks. Systems theory and cell biology have enjoyed a long relationship that, under the umbrella of systems biology, has received increasing attention in recent years. Systems biology is concerned with the dynamic behavior of biochemical reaction networks or pathways within cells and in cell populations. The biologist's conception of a pathway is equivalent to the control engineer's block diagram. A pathway map exhibits the names of the molecular components, whose interactions govern the basic cell functions. On the surface, control engineers and cell biologists speak a similar language, sharing basic vocabulary such as model, amplification, regulation, control, feedback, and nonlinear relationship, although the interpretation is often rather different. The most obvious difference between these knowledge domains is the fact that control and regulation are implicit in biological processes, and thus it is usually not possible to identify a control unit as a separate process. Unfortunately, it is not yet possible to generate quantitative and sufficiently rich time-series data from stimulus-response experiments to allow system identification in the traditional control-engineering sense.

In this paper we propose an observer for a class of nonlinear systems with internal structure. The observer combines ideas of parameter identifiers and high-gain observers. Special structure of the system allows the design to be carried out in stages providing some advantages over other observer design methods. The applicability of the observer is illustrated via an example in which the position and velocity of a point mass moving in the gravity field are estimated from perspective observations.

In this paper, we consider the problem of real-time planning and control of a robot manipulator in an unstructured workspace. The task we consider is to control the manipulator, such that the end-effector follows a path on an unknown surface, with the aid of a single camera assumed to be uncalibrated with respect to the robot coordinates. To accomplish a task of this kind, we propose a new control strategy based on multisensor fusion. We assume that three different sensors, i.e. encoders mounted at each joint of the robot with 6 d.o.f., a force-torque sensor mounted at the wrist of the manipulator and a visual sensor with a single camera fixed to the ceiling of the workcell, are available. Also, we assume that the contact point between the tool grasped by the end-effector and the surface is frictionless. To describe the proposed algorithm that we have implemented, first of all we decouple the vector space of control variables into two subspaces, and use one of the subspaces for controlling the magnitude of the contact force on the surface and the other subspace for controlling the constrained motion on the surface. In this way the control synthesis problem is decoupled and we are able to develop a new scheme that utilizes sensor fusion to handle uncalibrated parameters in the workcell and wherein the surface on which the task is to be performed is assumed to be visible, but has an apriori unknown position.

This technical note studies fixed-time consensus tracking with disturbance rejection for first-order multiagent systems. The communication topology among the leader and followers contains a directed spanning tree. The control input to the leader is time-varying and unknown to the followers, except that its upper bound is known a priori. A novel fixed-time protocol is devised based on discontinuous and nonlinear control. A fixed-time stability analysis for consensus tracking with disturbance rejection is completed as a one-step control process using nonsmooth analysis. An upper bound estimate for the settling time, independent of the initial conditions, is aesthetically pleasing, in comparison to previous results derived based on two-step control design technique using sliding modes. Finally, a numeric example confirms the theoretical results. [ABSTRACT FROM AUTHOR]