Obstacle Avoidance Scheme Based Elite Opposition Bat Algorithm for Unmanned Ground Vehicles

Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment without an onboard human operator but can be controlled autonomously using an obstacle avoidance system or by a human operator from a remote location. In this research, an obstacle avoidance scheme-based elite opposition bat algorithm (EOBA) for UGVs was developed. The obstacle avoidance system comprises a simulation map, a perception system for obstacle detection, and the implementation of EOBA for generating an optimal collision-free path that led the UGV to the goal location. Three distance thresholds of 0.1 m, 0.2 m, and 0.3 m was used in the obstacle detection stage to determine the optimal distance threshold for obstacle avoidance. The performance of the obstacle avoidance scheme was compared with that of bat algorithm (BA) and particle swarm optimization (PSO) techniques. The simulation results show that the distance threshold of 0.3 m is the optimal threshold for obstacle avoidance provided that the size of the obstacle does not exceed the size of the UGV. The EOBA based scheme when compared with BA and PSO schemes obtained an average percentage reduction of 21.82% in terms of path length and 60% in terms of time taken to reach the target destination. The uniqueness of this approach is that the UGV avoid collision with an obstacle at a distance of 0.3 m from nearby obstacles as against taking three steps backward before avoiding obstacl

Position and Trajectory Tracking Control for the Ball and Plate System using Mixed Sensitivity Problem

This paper presents the position and trajectory tracking control scheme for the ball and plate system (BPS) using the double feedback loop structure (a loop within a loop) for effective control of the system. The inner loop was designed using linear algebraic method by solving a set of Diophantine equations. The outer inner loop was designed using   sensitivity approach. Simulation results showed that the plate was stabilized at 0.3546 seconds, and the ball was able to settle at 1.7087 seconds, when given a circular trajectory of radius 0.4 m with an angular frequency of 1.57 rad/sec, with a trajectory tracking error of 0.0095 m, which shows that the controllers have adaptability, strong robustness and control performance for the ball and plate system.           

Development of a Dynamic Cuckoo Search Algorithm

This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo search algorithm (dCSA). The standard cuckoo search algorithm is a metaheuristics search algorithm that mimic the behavior of brood parasitism of some cuckoo species and Levy flight behavior of some fruit flies and birds. It, however uses fixed value for control parameters (control probability and step size) and this method have drawbacks with respect to quality of the solutions and number of iterations to obtain optimal solution. Therefore, the dCSA is developed to address these problems in the CSA by introducing random inertia weight strategy to the control parameters so as to make the control parameters dynamic with respect to the proximity of a cuckoo to the optimal solution. The developed dCSA was compared with CSA using ten benchmark test functions. The results obtained indicated the superiority of dCSA over CSA by generating a near global optimal result for 9 out of the ten benchmark test functions

Model Predictive Control of Blood Pressure and Urine Production Rate for a Physiological Patient Model

The research proposes the design of a model predictive control (MPC) for automatic drug dosing to regulate high blood pressure and urine production rate in an elderly patient. Combining hydrochlorothiazide and oxybutynin is commonly used for regulation of blood pressure in elderly patients. The patient’s model tries to captures the responses to the drugs as the blood pressure and urine production rates attains their various set-points. Hence, this research aims at improving the control scheme which ensured that these two physiological variables are regulated. Simulation was done in MATLAB/Simulink environment with the use of MPC Toolbox, and the controlled variables were constrained to operate at 80mmHg for blood pressure and between 24-49 ml/kg/hr for urine production rate respectively while the manipulated variables remained unconstrained. From the simulation results, the MPC controller achieved good set-point tracking and disturbance rejection, which is an indication of a healthy level of regulation within acceptable tolerances

Performance Comparison of the Ball and Beam System using Linear Quadratic Regulator Controller

This paper proposes the performance comparison of a linear quadratic regulator (LQR) controller for the ball and beam system (BBS). The BBS is a standard benchmark control system, which has two degree-of-freedom (2 DOF). It is an open loop and a highly nonlinear unstable system. This makes its parameter difficult to be estimated accurately, hence designing a controller for it is a challenging task. MatheThe BBS was modelled using Euler–Lagrange modeling technique, while the LQR controller was used for the stabilization of the ball on the beam. Simulation was done in MATLAB/Simulink 2022b environment, and the results simulated showed that for the two weighting matrices (Q and R), the state weighting matrix had a higher penalty on the ball displacement, ball velocity, beam angle, and beam angular velocity at lower values of Q. For the state weighting matrix had a better effect of penalty performance on the BBS with lower values. Also, as the diagonal element of the state weighting matrix Q increases from 0.1 to 20, the values of the optimal controller K increase, the reduced Ricatti matrix P increases, and the estimated eigenvalues E reduce. This implies that the ball displacement, ball velocity, beam angle, and beam angular velocity are better at lower values of Q

Development of a Dynamic Cuckoo Search Algorithm

This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo search algorithm (dCSA). The standard cuckoo search algorithm is a metaheuristics search algorithm that mimic the behavior of brood parasitism of some cuckoo species and Levy flight behavior of some fruit flies and birds. It, however uses fixed value for control parameters (control probability and step size) and this method have drawbacks with respect to quality of the solutions and number of iterations to obtain optimal solution. Therefore, the dCSA is developed to address these problems in the CSA by introducing random inertia weight strategy to the control parameters so as to make the control parameters dynamic with respect to the proximity of a cuckoo to the optimal solution. The developed dCSA was compared with CSA using ten benchmark test functions. The results obtained indicated the superiority of dCSA over CSA by generating a near global optimal result for 9 out of the ten benchmark test functions

Obstacle Avoidance-Based Autonomous Navigation of a Quadrotor System

Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep

Moving Horizon Estimator for Space Vehicle Dynamics with Measurement Noise in Close Propinquity Operation

Analyzing the impact of measurement noise on space vehicle dynamics is vital for understanding its implications in proximity missions. Despite researcher's focus on addressing position and velocity challenges during cooperative operations, few studies explore measurement noise effects in dynamic environments. This study introduces a methodology, primarily utilizing the Model Predictive Control-Based Moving Horizon Estimator (MPC-MHE) with Euclidean navigation constraints, to optimize the estimation of unmeasured state variables in space vehicle dynamics. Results show the efficacy of the moving-horizon estimator inaccurate estimation, outperforming LQ-MPC and IDVD in assessing unmeasured states with minimal control effort under measurement noise. The proposed technique is further compared with LQ-MPC/DH, LQ-MPC/RH, and NMPC in three scenarios, revealing superior performance in time to dock and control effort metrics. The method demonstrates an ARPI of 42.33  and 41.70  in Case A, 34.51  and 33.65  in Case B, and 32.90  and 41.25  in Case C for time to dock and control effort, respectively. Moreover, the moving horizon estimator exhibits a 95  coefficient of determination for unmeasured state estimates compared to ground truth data, highlighting its effectiveness in mitigating measurement noise challenges in space vehicle dynamics