New nonlinear control algorithms for multiple robot arms

A mathematical model for the steering control of an automobile is described. The structure of the model derives from linear optimal discrete time preview control theory but it is non-linear. Its parameter values are obtained by heuristic methods, using insight gained from the linear optimal control theory. The driver model is joined to a vehicle dynamics model and the path tracking performance is demonstrated, using moderate manoeuvring and racing speeds. The model is shown to be capable of excellent path following and to be robust against changes in the vehicle dynamics. Application to the simulation of manoeuvres specified by an ideal vehicle path and further development of the model to formalise the derivation of its parameter values and to put it to other uses are discussed.

Linear optimal control theory is applied to the control synthesis of a high bypass ratio, variable pitch, turbofan engine. The basic control philosophy is to use only a low order dynamic model of the plant coupled with the concept of integral-output states so as to maintain control simplicity yet guarantee integral control of thrust, turbine temperature and other important engine outputs. Linear simulation results indicate that the control system developed provides rapid control of small thrust perturbations and quickly eliminates the effect of unmodelled thrust and temperature disturbances. Large thrust accelerations are obtained in about one half second while the control maintains negligible overshoot in temperature and stall margins.

A EROSPACE engineering applications greatly stimulated the development of optimal control theory during the 1950s and 1960s, where the objective was to drive the system states in such a way that some defined cost was minimized. This turned out to have very useful applications in the design of regulators (where some steady state is to be maintained) and in tracking control strategies (where some predetermined state trajectory is to be followed). Among such applications was the problem of optimal flight trajectories for aircraft and space vehicles. Linear optimal control theory in particular has been very well documented and widely applied, where the plant that is controlled is assumed linear and the feedback controller is constrained to be linear with respect to its input. However, the availability of powerful low-cost microprocessors has spurred great advantages in the theory and applications of nonlinear control. The competitive era of rapid technological change, particularly in aerospace exploration, now demands stringent accuracy and cost requirements in nonlinear control systems. This has motivated the rapid development of nonlinear control theory for application to challenging, complex, dynamical real-world problems, particularly those that bear major practical significance in aerospace, marine, and defense industries. Infinite-time horizon nonlinear optimal control (ITHNOC) presents a viable option for synthesizing stabilizing controllers for nonlinear systems by making a state-input tradeoff, where the objective is to minimize the cost given by a performance index. The original theory of nonlinear optimal control dates from the 1960s. Various theoretical and practical aspects of the problem have been addressed in the literature over the decades since. In particular, the continuous-time nonlinear deterministic optimal control problem associated with autonomous (time-invariant) nonlinear regulator systems that are affine (linear) in the controls has been studied by many authors. The long-established theory of optimal control offers quite mature and well-documented techniques for solving this control-affine nonlinear optimization problem, based on dynamic programming or calculus of variations, but their application is generally a very tedious task. Bellman’s dynamic programming approach reduces to solving a nonlinear first-order partial differential equation (PDE), expressed by the Hamilton–Jacobi–Bellman (HJB) equation. The solution to the HJB equation gives the optimal performance/cost value (or storage) function and determines an optimal control in feedback form under some smoothness assumptions. Alternatively, in the classical calculus of variations, optimal control problems can be characterized locally in terms of the Hamiltonian dynamics arising from Pontryagin’s minimum principle. These are the characteristic equations of the HJB PDE, which result in a nonlinear, constrained two-point boundary value problem (TPBVP) that, in general, can only be solved by successive approximation of the optimal control input using iterative numerical techniques for each set of initial conditions. Numerically, even though the nonlinear TPBVP is somewhat easier to solve than the HJBPDE, control signals can only be determined offline and are thus best suited for feedforward control of plants for which the state trajectories are known a priori. Therefore, contrary to the dynamic programming approach, the resultant control law is not generally in feedback form. Open-loop control, however, is sensitive to random disturbances and requires that the initial state be on the optimal trajectory. In contrast, nonlinear optimal feedback has inherent robustness properties (inherent in the sense that it is obtained by ignoring uncertainty and disturbances). The potential difficulty with the HJB approach is that no efficient algorithm is available to solve the PDE when it is nonlinear and the problem dimension is high, making it impossible to derive exact expressions for optimal controls for most nontrivial problems of interest. The optimal can only be computed in special cases, such as linear dynamics and quadratic cost, or very low-dimensional systems. In particular, if the plant is linear time invariant (LTI) and the (infinite-time) performance index is quadratic, then the corresponding HJB equation for this infamous linear-quadratic regulator (LQR) problem reduces to an algebraic Riccati equation (ARE). Contrary to the well-developed and widely applied theory and computational tools for theRiccati equation (for example, see [1]), theHJB equation is difficult, if not impossible, to solve for most practical applications. The exact solution for the optimal control policies is very complex

The question of control and stabilization of flexible space robots is considered. Although, this approach is applicable to space robots of other configurations, for definiteness, a flexible planar two-link robot, mounted on a rigid floating platform in space, is considered. The robotic arm has two revolute joints and its links undergo elastic deformation in the plane of rotation. Based on nonlinear inversion technique, a control law is derived for controlling output variables describing the position and orientation of the platform and the joint angles of the robot. Although, the inverse controller accomplishes reference trajectory tracking, it excites the elastic modes of the arm. Using linear quadratic optimal control theory, a composite stabilizer for stabilization of the rigid and flexible modes and a decoupled flexible mode stabilizer are designed for regulating the end point of the robot to the target point and vibration suppression. Stabilization using only elastic mode velocity feedback is also considered. For large maneuvers, first the inverse controller is active, and the stabilizer is switched for regulation when the motion of the robot lies in the neighborhood of the terminal equilibrium state. Simulation results are presented to show that in the closed-loop system including the inverse controller and the stabilizer, trajectory tracking and stabilization of elastic modes are accomplished.

The development of on ramp metering control strategies for a freeway can be aided by the well-developed theory of linear optimal control. The recently developed variable model appears to be best suited for this purpose. In this model the freeway is partitioned into lengths of about 1/2 mile and traffic is described in terms of the aggregate variables section speed, section density, and volume. This model and linear optimal control theory are used to developed an on ramp metering strategy which is designed to maintain freeway conditions near nominal conditions in the presence of disturbances. An illustrative design for a segment of the San Diego freeway in Los Angeles is provided. The companion problem of processing freeway sensor data to provide current estimates of the aggregate variables can be approached through standard techniques in estimation theory. Methods of attacking this portion of the freeway surveillance problem are presented.

The development of on-ramp metering control strategies for a freeway can be aided by the well-developed theory of linear optimal control. However, an adequate model of an appropriate form must be available. Recently developed "aggregate variable" models appear to be best suited to this purpose. In these models the freeway is sectioned into lengths of about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\frac{1}{2}</tex> mi and traffic is described in terms of the aggregate variables section speed, section density, and volume. The model is used to formulate a linear regulator problem with quadratic cost for the purpose of determining a feedback control rule which returns the freeway to nominal conditions after a disturbance. The solution of the regulator problem involves considerable computation difficulty when the order of the system is large. In a large class of applications the optimal control law has a local structure which can be exploited in a computationally efficient manner to yield a suboptimal control with little loss of system performance. A new technique, the augmentation method, is presented for constructing a suboptimal control from a set of optimal controls for low-order sub-systems. New results are presented which provide a rational and elegant measure for the additional cost associated with an arbitrary stabilizing suboptimal control. Additional results pertain especially to the augmentation procedure. The models and suboptimal control scheme are applied to a specific detailed design for an 8-mi segment of the San Diego Freeway in Los Angeles. Simulations are shown illustrating the effectiveness of such traffic-responsive controls in clearing up congestion due to traffic incidents.

The design method of a quantitative robust optimal control system is developed. Wiener-Hopf linear quadratic optimal control and quantitative feedback theory are applied to achieve the optimal system control. In this design, two frequency-dependent weighting functions are shaped and two-degree-of-freedom compensators are designed to achieve the robust optimal control.

SUMMARYAlmost all industrial robot applications in use today are controlled using a control law that is simple and computationally efficient, local joint error feedback. When two or more open chain manipulators cooperate to manipulate the same object - such as in mechanical grippers, walking machines, and cooperating manipulator systems - closed kinematic chain, redundantly actuated mechanisms are formed. Control approaches for this type of system focus on the more computationally intensive computed torque or inverse plant control laws, due to the concern over instability caused by the unspecified distribution of control forces in the redundant actuator space, and due to the constrained motion caused by the closed kinematic chains.

Examines the application of linear optimal control theory to a low-order nonlinear chaotic convection problem. Linear control feedback is found to be fully effective only when it is switched off while the state is far from the desired equilibrium point, relying on the attractor of the system to bring the state into a neighborhood of the equilibrium point before control is applied. Linear estimator feedback is found to be fully effective only when (a) the Lyapunov exponent of the state estimation error is negative, indicating that the state estimate converges to the uncontrolled state, and (b) the estimator is stable in the vicinity of the desired equilibrium point. The aim in studying the present problem is to understand better some possible pitfalls of applying linear feedback to nonlinear systems in a low-dimensional framework. Such an exercise foreshadows problems likely to be encountered when applying linear feedback to infinite-dimensional nonlinear systems such as turbulence. It is important to understand these problems and the remedies available in a low-dimensional framework before moving to more complex systems such as turbulence.

This chapter provides a self-contained overview of modern linear feedback control theory. We focus on key methods for laminar and transitional flow stabilization, introducing concepts from optimal control theory and system identification to build intuition. Optimal regulators and filters are discussed, along with system identification.

Dynamic Vibration Absorbers (DVAs) have been used to attenuate vibrations in various types of mechanical Systems. In its simplest form, a DVA is formed by an association of passive elements (inertia, stiffness and damping). The values of these parameters are selected so as to tune the DVA to a given value of the excitation frequency, assumed to be fixed. As a result, the attenuation capability of a passive DVA significantly decreases as the excitation frequency deviates from the nominal tuning frequency. To avoid this drawback, active and adaptive DVAs have been extensively studied lately, in an attempt to achieve larger effective bandwidths and self tunning capability. Active DVAs contain, besides the passive elements, an actuator wich applies a control force determined by an appropriate control law. Adaptive DVAs are understood as those constructed in such a way that the values of their physical parameters can be adjusted according to well-defined laws. In this work, some configurations of active and adaptive DVAs are assessed, namely: a) an active DVA using as feedback signal the time-delayed displacement response of the reactive mass (delayed resonator); b) a novel active DVA using a control law in wich the control force is expressed as a linear combination of the displacement, velocity and acceleration of the reactive mass, relative to the primary mass; c) an active DVA exploring linear quadratic optimal control theory; d) a vibrating string-type adaptive DVA; e) a pendulum-type adaptive DVA; f) an adaptive DVA formed from a beam with piezoelectric patches. For each configuration the theoretical foundations are first developed. Then, numerical applications are presented to assess their main features and performance.

Based on a differential geometric feedback linearization technique for nonlinear time-varying systems, a dynamic force control method for robot arms is developed. It uses active force-moment measurements at the robot wrist. The controller design fully incorporates the robot-arm dynamics and is so general that it can be reduced to pure position control, hybrid position/force control, and pure force control. The controller design is independent of the tasks to be performed. Computer simulations show that the controller improves the position error by a factor of ten in cases in which position errors generate force measurements. A theorem on linearization of time-varying system is also presented. >

Designing active flutter suppression for high-dimensional aeroelastic systems involving a control delay

The structure of the time-optimal control law for multiple robot arms cooperatively moving a common object along a specified path with the control torque constraints is addressed. The overall mechanical system is modeled by considering the arms as closed kinematic chains using the Lagrange formulation. This results in a reduced-order dynamic model of the multi-arm system. By parameterizing the resulting dynamic model along a given path, the original higher order optimal control problem with state constraints (path constraints) is transformed into a problem of a double integrator system with state-dependent control constraints that are determined by a linear programming approach. It is then shown that the number of saturated actuators on any finite time interval along the optimal trajectory is (1+3(D-1)(m-1)), where D=2 in 2-dimensional Cartesian space, D=3 in 3-dimensional space, and m is the number of robot arms in the system. The theoretical result is validated by a numerical example.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The process of connecting task descriptions originating from machine intelligence planning programs to the mechanization of feedback control of robot arms is analyzed. It is shown in this paper that control theories and practices can be extended to a higher level where feedback control of robot arms directly can respond to work space task commands provided that the work space task as a command is given in the form of a closed function of time. A general mathematical procedure using tools from differential geometry is introduced for synthesizing task space motion planning so that the planned motion can be used as a direct input to the robot arm feedback control system to achieve desired robot hand motion. By definition, "intelligent control" is being manifested through robot performance in the task space relative to task space commands. Thus, the capability of implementing feedback control of robot arms directly driven by appropriate task descriptions in the workspace as commands is a step toward intelligent control.