This paper presents the control design of a nonholonomic mobile robot with differential drive using control strategies on a linearized space state error model. In this case, a diagonal multi-variable model is obtained for which a decentralized PI controller may be designed. In this paper, PI tuning is performed through a LQR problem whose feedback gains are set as the proportional gain of the PI controller. The use of an Inertial Measuring Unit (IMU) allows for a precise posture feedback by using a Kalman Filter on the output of the sensors. For such purpose, a Sensor Fusion technique is also needed in order to combine multiple sensor output so that physical limitations of each sensor may be compensated.

This paper presents the control design of a nonholonomic mobile robot with differential drive using control strategies on a linearized space state error model. In this case, a diagonal multi-variable model is obtained for which a decentralized PI controller may be designed. In this paper, PI tuning is performed through a LQR problem whose feedback gains are set as the proportional gain of the PI controller. The use of an Inertial Measuring Unit (IMU) allows for a precise posture feedback by using a Kalman Filter on the output of the sensors. For such purpose, a Sensor Fusion technique is also needed in order to combine multiple sensor output so that physical limitations of each sensor may be compensated.

This work presents a study on differential drive wheeled mobile robots regarding its localization estimation using sensor fusion techniques and control over a reference trajectory. The robot’s posture is an extremely important variable to be estimated, specially for autonomous mobile robots as it has to drive along a path without manual intervention. Posture estimation provided by individual sensors has shown to be inaccurate or straightforward mismatched, leading to a need of data fusion from different sources. Sensors such as accelerometer, gyroscope and magnetometer each have its own intrinsic constructive limitations. However, by combining all their flaws into a linear model allows optimal estimators, such as Kalman Filter (KF), to be used and produce estimates close to real life behavior. For the trajectory tracking and disturbance rejection, a linear control strategy for a linearized mobile robot model is applied. The system is modeled with error states to be carried out by a Linear Quadratic Regulator (LQR) controller along with a feedforward reference control action so that the reference trajectory is accordingly tracked.

This paper presents the development and trajectory tracking control of a nonholonomic mobile robot with differential drive. The used control system is composed of two loops cascaded, with internal PID controlling the speed of the two wheels and an external loop controlling the robot position and orientation. The second one is a feedfoward kinematics controller which is tuned according to desired closed-loop characteristics specified by the user. The control strategies were embedded in a digital module based in a 32-bit ARM microcontroller. In order to evaluate the performance of the developed robot, trajectory tracking tests were carried out for different trajectories and initial conditions. Preliminary experimental results are presented for reference trajectories without obstacles.

This work presents an LPV (Linear Parameter Varying) controller design for the trajectory track- ing problem of nonholonomic wheeled mobile robots (NWMR). The scheduling varying parameters are defined as the feedforward velocities which the robot has to track according to a desired trajectory. The proposed model also includes the robot’s motion over a tilted environment. A tilt-compensation scheme is used to maintain the ground speed of the robot if the robot is subjected to an inclination. The compensation is computed on the feedforward velocities. For practical purposes, the inclination angle is measured by an Inertial Measuring Unit. Thus, a sensor fusion model is proposed to fuse data from an accelerometer and a gyroscope. Simulation results are presented in order to evaluate the performance of the controller, sensor fusion and tilt-compensation models.

In this paper it is investigated the trajectory tracking control of a wheeled mobile robot using a laser rangefinder sensor to estimate the robot position and orientation (posture). This technique is compared with the conventional odometry method where the encoders data are used for posture estimation. The paper also presents the description of the robot as well as its mathematical modeling. Two controllers in cascade were used to implement the tracking control. A kinematics controller generates the references for internal motor speed controllers. Experimental results in an indoor environment are presented for trajectories with no obstacles.

This work presents an application of an adaptive control strategy for reference trajectory tracking of a mobile robot with non-holonomic constraints. The controller consists of two cascade control loops, whose first loop is responsible for the wheeled motor speed control, and the second is responsible for a non-linear control action that allows reference trajectory tracking. In order to maintain performance specifications when the robot is subjected to internal disturbances or model uncertainty, an auto-tuning non-linear adaptive controller is proposed. It also allows the robot to adjust to different types of reference trajectory. Results of both computational simulations and experimental tests in a real mobile robot show the advantages and disadvantages of the proposed adaptive controller.

With the use of matrix inequalities, this work proposes a gain as a function of the control variables, that is able to guarantee the stability of the model and reduce the disturbances regardless of the condition in which the aircraft is involved. A study is performed around the linearized equilibrium conditions for a Lynx ZD559 model. In this, the independent control variable is the forward speed of the aircraft. The results obtained from a stationary controller, one with commutable variation and one of linear variation elucidate the ability of the last controller to operate over the entire spectrum of speed variation by presenting an acceptable transient dynamics and disturbance rejection.

This work proposes a dead-time compensator (DTC) with feedforward action for first-order plus dead-time processes (FOPDT) with measurable disturbance. The proposed controller structure is based on both feedforward Smith Predictor and the Simplified Dead-time Compensator (SDTC). Simulation results compared with other recent literature propositions show the effectiveness of the proposed controller.

Despite many advances in the field of dead-time compensators (DTCs) for unstable first-order plus dead- time (UFOPDT) processes, the tuning, in general, is manually carried out. Therefore, this paper proposes simple tuning rules for a DTC intended to UFOPDT processes. The rules are based on the relative dead time, desired closed-loop time constant, and achievable robustness. Besides, as the practical implemen- tation is always in the discrete-time domain, a method to choose the sampling period for UFOPDT pro- cesses is presented. Four simulation examples from the literature are used to show the advantages of the proposed method. In addition, an experiment with a propeller pendulum is performed to confirm such advantages in the control of a real unstable process with dead time.