Discrete-time Integral Control Research Articles

Robust adaptive predictive digital controller with integral action for set-point regulation of saturated uncertain systems

A robust adaptive digital controller is systematically investigated on set-point regulation in uncertain systems under input saturation. So, an optimization strategy will be derived to solve the reference regulation robustly in the discrete-time systems. To this purpose, a discrete-time integral controller is considered through a fixed state-feedback structure. Then, its coefficients would be numerically quantified at each sample time by means of a minimization. The formulation and merit of the addressed predictive control method are theoretically proved in the paper. Then the control technique is simulated in a discrete-time equation. Additionally, some numerical results are executed to declare the remarkability and vigor of the recommended algorithm against uncertainties, actuator saturation, and disturbance in contrast to the existing control ways.

Stair Climbing via Successive Perching

Stairs are a primary challenge for mobile robots navigating indoor human environments. Stair climbing is a useful, if not necessary, capability for mobile robots in urban search and rescue, security, cleaning, telepresence, elder care, and other applications. Existing stair climbing robots are large, expensive, and not always reliable, especially when descending stairs. In this paper, we present a novel approach for stair climbing that is achievable by a small mobile robot with minimal actuators and sensors and, thus, cost. The proposed robot has articulated tread assemblies on either side of a chassis. Using feedback control, the robot can balance on the edge of a single step. As the robot drives up the step, the chassis pivots to maintain the center of mass directly above the contact point. The dynamics of the system are derived with the Lagrangian method, and a discrete-time integral controller with friction compensation is designed to stabilize a stair climbing trajectory. The algorithms used to estimate the state of the system with low-cost noisy proprioceptive sensors are explained in detail. No external motion capture system is used. Simulation results are compared with successful experimental results.

Discrete-Time and Sampled-Data Low-Gain Control of Infinite-Dimensional Linear Systems in the Presence of Input Hysteresis

We introduce a general class of causal dynamic discrete-time nonlinearities which have certain monotonicity and Lipschitz continuity properties. In particular, the discretizations of a large class of continuous-time hysteresis operators obtained by applying the standard sampling and hold operations belong to this class. It is shown that closing the loop around a power-stable, linear, infinite-dimensional, discrete-time, single-input, single-output system, subject to an input nonlinearity from the class under consideration and compensated by a discrete-time integral controller, guarantees asymptotic tracking of constant reference signals, provided that (a) the positive integrator gain is sufficiently small and (b) the reference value is feasible in a very natural sense. We apply this result in the development of sampled-data low-gain integral control for exponentially stable, regular, linear, infinite-dimensional, continuous-time systems subject to input hysteresis.