The proportional-integral-derivative (PID) is still the most common controller and stabilizer used in industry due to its simplicity and ease of implementation. In most of the real applications, the controlled system has parameters which slowly vary or are uncertain. Thus, PID gains must be adapted to cope with such changes. In this paper, adaptive PID (APID) controller is proposed using the recursive least square (RLS) algorithm. RLS algorithm is used to update the PID gains in real time (as system operates) to force the actual system to behave like a desired reference model. Computer simulations are given to demonstrate the effectiveness of the proposed APID controller on SISO stable and unstable systems considering the presence of changes in the systems parameters.

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-This study presents a well-developed optimization methodology based on the dynamic inertia weight Artificial Bee Colony algorithm (ABC) to design an optimal PID controller for a robotic arm manipulator. The dynamical analysis of robotic arm manipulators investigates a coupling relation between the joint torques applied by the actuators and the position and acceleration of the robot arm. An optimal PID control law is obtained from the proposed (ABC) algorithm and applied to the robotic system. The designed controller optimizes the trajectory of the robot's end effector for a time-variant input and makes the robot robust in the presence of external disturbance.

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Summary In this paper, a new technique is developed to estimate the states of both deterministic and stochastic discrete‐time nonlinear systems. The stochastic system can be uncertain. The corrupting input and/or the output noise vectors may be either Gaussian or non‐Gaussian zero‐mean sequences. The proposed filter is based on pole placement technique, in which a set of constraints are imposed of the estimated outputs. The stability of the estimator is rigorously analyzed. An extension to the developed estimator is proposed to deal with constrained estimation problems. To illustrate the effectiveness and simplicity of the developed technique, illustrative examples are presented. Simulations results show that, for the deterministic case, the developed procedure leads to better results when compared with High‐gain observer, Thau's filter, and the Iterative Regularized Least Square estimator. On the other hand, for the stochastic case, the proposed estimator is superior to the extended Kalman filter and the iterative constrained estimator (ICE) with non‐ideal situations where the statistics of the noise signals and/or the system parameters are unknown, or the noise signals are non‐Gaussian. Copyright © 2016 John Wiley & Sons, Ltd.